feat: ja4-platform monorepo — 5 services unified, tests & RPM builds standardized

Services:
- ja4sentinel: TLS/JA4 fingerprint capture daemon (Go, libpcap)
- logcorrelator: JA4 log correlation engine (Go, ClickHouse)
- mod_reqin_log: Apache module (C, JSON request logging)
- bot_detector: ML bot detection pipeline (Python)
- dashboard: FastAPI/Streamlit analytics UI (Python)

Shared libraries:
- shared/go/ja4common: logger, config, shutdown, ipfilter (Go module)
- shared/python/ja4_common: ClickHouseClient, ClickHouseSettings (Python package)
- shared/clickhouse/: canonical SQL migrations (10 files)

Build & packaging:
- Unified 3-stage Dockerfile.package for Go RPMs (el8/el9/el10)
- go.work workspace linking sentinel, correlator, ja4common
- Makefile with test-all, build-all, rpm-* targets

Fixes applied:
- go.work: 1.21 → 1.24.6 (required by sentinel)
- correlator Dockerfiles: golang:1.21 → golang:1.24
- replace directives in go.mod for ja4common local path
- pyproject.toml: setuptools.backends → setuptools.build_meta
- Removed static libpcap linking (unavailable on Rocky 9)
- Fixed data races in output/writers_test.go (sync.Mutex + atomic.Int32)
- Rewrote corrupted test files (logger_test.go × 2)

Test coverage:
- correlator: 67.1% total (unixsocket 80.5%, config 91.7%, app 83.3%, multi 87.7%, stdout 100%)
- sentinel: all 10 packages pass (api, capture, config, fingerprint, ipfilter, logging, output, tlsparse)

Documentation:
- README.md + docs/ (architecture, development, 5 services, shared libs, DB schema & migrations)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

services/dashboard/backend/__init__.py

@@ -0,0 +1 @@
+# Backend package

services/dashboard/backend/config.py

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+"""
+Configuration du Dashboard Bot Detector
+"""
+from pydantic_settings import BaseSettings
+
+
+class Settings(BaseSettings):
+    # ClickHouse
+    CLICKHOUSE_HOST: str = "clickhouse"
+    CLICKHOUSE_PORT: int = 8123
+    CLICKHOUSE_DB: str = "mabase_prod"
+    CLICKHOUSE_USER: str = "admin"
+    CLICKHOUSE_PASSWORD: str = ""
+
+    # API
+    API_HOST: str = "0.0.0.0"
+    API_PORT: int = 8000
+
+    # CORS
+    CORS_ORIGINS: list = ["http://localhost:3000", "http://127.0.0.1:3000"]
+
+    class Config:
+        env_file = ".env"
+        case_sensitive = True
+
+
+settings = Settings()

services/dashboard/backend/database.py

@@ -0,0 +1,7 @@
+"""
+ClickHouse connection — delegates to ja4_common shared client.
+"""
+from ja4_common.clickhouse import get_client as _get_client, ClickHouseClient
+
+# Re-export for backward compatibility with existing route imports
+db: ClickHouseClient = _get_client()

services/dashboard/backend/main.py

@@ -0,0 +1,237 @@
+"""
+Bot Detector Dashboard - API Backend
+FastAPI application pour servir le dashboard web
+"""
+import logging
+from contextlib import asynccontextmanager
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from fastapi.responses import FileResponse
+import os
+
+from .config import settings
+from .database import db
+from .routes import metrics, detections, variability, attributes, analysis, entities, incidents, audit, reputation, fingerprints
+from .routes import bruteforce, tcp_spoofing, header_fingerprint, heatmap, botnets, rotation, ml_features, investigation_summary, search, clustering
+
+# Configuration logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Gestion du cycle de vie de l'application"""
+    # Startup
+    logger.info("Démarrage du Bot Detector Dashboard API...")
+    logger.info(f"ClickHouse: {settings.CLICKHOUSE_HOST}:{settings.CLICKHOUSE_PORT}")
+    logger.info(f"Database: {settings.CLICKHOUSE_DB}")
+    
+    # Tester la connexion ClickHouse
+    try:
+        client = db.connect()
+        client.ping()
+        logger.info("Connexion ClickHouse établie avec succès")
+    except Exception as e:
+        logger.error(f"Échec de connexion ClickHouse: {e}")
+        raise
+    
+    yield
+    
+    # Shutdown
+    logger.info("Arrêt du Bot Detector Dashboard API...")
+    db.close()
+
+
+# Création de l'application FastAPI
+OPENAPI_TAGS = [
+    {
+        "name": "Metrics",
+        "description": "Métriques globales : comptages, niveaux de menace, baseline et distribution des scores ML.",
+    },
+    {
+        "name": "Detections",
+        "description": "Liste paginée et filtrée des anomalies détectées par le modèle ML. Supporte tri, recherche texte et regroupement par IP.",
+    },
+    {
+        "name": "investigation",
+        "description": (
+            "**Point d'entrée principal pour l'analyse d'une IP.** "
+            "Agrège en un seul appel : score ML, brute-force, spoofing TCP, rotation JA4, persistance et timeline 24h. "
+            "Retourne un `risk_score` heuristique de 0 à 100."
+        ),
+    },
+    {
+        "name": "Reputation",
+        "description": "Réputation externe d'une IP via IP-API.com et IPinfo.io (sans clé API). Détecte proxies, VPN, Tor, hébergeurs.",
+    },
+    {
+        "name": "Analysis",
+        "description": "Analyses approfondies par IP : subnet, pays, empreintes JA4, user-agents, recommandation SOC et gestion des classifications.",
+    },
+    {
+        "name": "Entities",
+        "description": "Investigation par entité (IP, JA4, subnet, user-agent, host). Retourne détections associées, user-agents, chemins, paramètres et entités liées.",
+    },
+    {
+        "name": "Incidents",
+        "description": "Clusters d'incidents actifs regroupés par similarité comportementale. Permet la classification et le suivi des incidents.",
+    },
+    {
+        "name": "Fingerprints",
+        "description": "Analyse des empreintes JA4/TLS : spoofing, matrice JA4↔UA, user-agents suspects, cohérence par IP, JA4 légitimes et corrélation ASN.",
+    },
+    {
+        "name": "Bruteforce",
+        "description": "Détection des attaques brute-force : cibles, attaquants, timeline et détail par host.",
+    },
+    {
+        "name": "TCP Spoofing",
+        "description": "Détection du spoofing TCP/OS fingerprinting : vue d'ensemble, liste et matrice TTL×MSS.",
+    },
+    {
+        "name": "Header Fingerprint",
+        "description": "Clusters de fingerprints d'en-têtes HTTP suspects et IPs associées.",
+    },
+    {
+        "name": "Heatmap",
+        "description": "Heatmap horaire du trafic, top hosts et matrice activité/heure.",
+    },
+    {
+        "name": "Botnets",
+        "description": "Détection de botnets : spread JA4, distribution géographique par JA4, résumé global.",
+    },
+    {
+        "name": "Rotation",
+        "description": "Détection de la rotation JA4 (évasion de détection), menaces persistantes, historique JA4 par IP et score de sophistication.",
+    },
+    {
+        "name": "ML Features",
+        "description": "Données brutes du modèle ML : top anomalies, radar par IP, distribution des scores, tendances, features B et scatter plot.",
+    },
+    {
+        "name": "Attributes",
+        "description": "Listes des valeurs distinctes d'attributs (JA4, user-agents, ASN, pays…) avec comptages.",
+    },
+    {
+        "name": "Variability",
+        "description": "Variabilité comportementale : IPs par attribut, attributs par valeur, analyse des user-agents.",
+    },
+    {
+        "name": "Clustering",
+        "description": "Clustering K-Means des IPs sur les features ML. Statut du cache, clusters, points et IPs par cluster.",
+    },
+    {
+        "name": "Search",
+        "description": "Recherche rapide cross-entités (IP, JA4, host, user-agent, pays, ASN).",
+    },
+    {
+        "name": "Audit",
+        "description": "Journal d'audit SOC : création de logs, consultation filtrée, statistiques et activité par utilisateur.",
+    },
+]
+
+app = FastAPI(
+    title="Bot Detector Dashboard API",
+    description=(
+        "API REST du **Bot Detector SOC Dashboard**.\n\n"
+        "Permet d'interroger la base ClickHouse (`mabase_prod`) pour visualiser et analyser "
+        "les détections de bots générées par le service `bot_detector_ai`.\n\n"
+        "**Endpoint clé :** `GET /api/investigation/{ip}/summary` — synthèse complète en un appel.\n\n"
+        "Documentation interactive : `/docs` (Swagger UI) · `/redoc` (ReDoc)"
+    ),
+    version="1.0.0",
+    openapi_tags=OPENAPI_TAGS,
+    lifespan=lifespan,
+)
+
+# Configuration CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=settings.CORS_ORIGINS,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Enregistrement des routes
+app.include_router(metrics.router)
+app.include_router(detections.router)
+app.include_router(variability.router)
+app.include_router(attributes.router)
+app.include_router(analysis.router)
+app.include_router(entities.router)
+app.include_router(incidents.router)
+app.include_router(audit.router)
+app.include_router(reputation.router)
+app.include_router(fingerprints.router)
+app.include_router(bruteforce.router)
+app.include_router(tcp_spoofing.router)
+app.include_router(header_fingerprint.router)
+app.include_router(heatmap.router)
+app.include_router(botnets.router)
+app.include_router(rotation.router)
+app.include_router(ml_features.router)
+app.include_router(investigation_summary.router)
+app.include_router(search.router)
+app.include_router(clustering.router)
+
+
+# Chemin vers le fichier index.html du frontend (utilisé par serve_frontend et serve_spa)
+_FRONTEND_INDEX = os.path.join(os.path.dirname(__file__), "..", "frontend", "dist", "index.html")
+
+# Route pour servir le frontend
+@app.get("/")
+async def serve_frontend():
+    """Sert l'application React"""
+    if os.path.exists(_FRONTEND_INDEX):
+        return FileResponse(_FRONTEND_INDEX)
+    return {"message": "Dashboard API - Frontend non construit. Voir /docs pour l'API."}
+
+
+# Servir les assets statiques
+_assets_path = os.path.join(os.path.dirname(__file__), "..", "frontend", "dist", "assets")
+if os.path.exists(_assets_path):
+    try:
+        app.mount("/assets", StaticFiles(directory=_assets_path), name="assets")
+    except Exception as _e:
+        logger.warning(f"Impossible de monter les assets statiques : {_e}")
+
+
+# Health check
+@app.get("/health")
+async def health_check():
+    """Endpoint de santé pour le health check Docker"""
+    try:
+        db.connect().ping()
+        return {"status": "healthy", "clickhouse": "connected"}
+    except Exception as e:
+        return {"status": "unhealthy", "clickhouse": "disconnected", "error": str(e)}
+
+
+# Route catch-all pour le routing SPA (React Router) - DOIT ÊTRE EN DERNIER
+# Sauf pour /api/* qui doit être géré par les routers
+@app.get("/{full_path:path}")
+async def serve_spa(full_path: str):
+    """Redirige toutes les routes vers index.html pour le routing React"""
+    # Ne pas intercepter les routes API
+    if full_path.startswith("api/"):
+        raise HTTPException(status_code=404, detail="API endpoint not found")
+
+    if os.path.exists(_FRONTEND_INDEX):
+        return FileResponse(_FRONTEND_INDEX)
+    return {"message": "Dashboard API - Frontend non construit"}
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(
+        "main:app",
+        host=settings.API_HOST,
+        port=settings.API_PORT,
+        reload=True
+    )

services/dashboard/backend/models.py

@@ -0,0 +1,322 @@
+"""
+Modèles de données pour l'API
+"""
+from pydantic import BaseModel, Field, ConfigDict
+from typing import Optional, List, Dict, Any
+from datetime import datetime
+from enum import Enum
+
+
+class ThreatLevel(str, Enum):
+    CRITICAL = "CRITICAL"
+    HIGH = "HIGH"
+    MEDIUM = "MEDIUM"
+    LOW = "LOW"
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# MÉTRIQUES
+# ─────────────────────────────────────────────────────────────────────────────
+
+class MetricsSummary(BaseModel):
+    total_detections: int
+    critical_count: int
+    high_count: int
+    medium_count: int
+    low_count: int
+    known_bots_count: int
+    anomalies_count: int
+    unique_ips: int
+
+
+class TimeSeriesPoint(BaseModel):
+    hour: datetime
+    total: int
+    critical: int
+    high: int
+    medium: int
+    low: int
+
+
+class MetricsResponse(BaseModel):
+    summary: MetricsSummary
+    timeseries: List[TimeSeriesPoint]
+    threat_distribution: Dict[str, int]
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# DÉTECTIONS
+# ─────────────────────────────────────────────────────────────────────────────
+
+class Detection(BaseModel):
+    detected_at: datetime
+    src_ip: str
+    ja4: str
+    host: str
+    bot_name: str
+    anomaly_score: float
+    threat_level: str
+    model_name: str
+    recurrence: int
+    asn_number: str
+    asn_org: str
+    asn_detail: str
+    asn_domain: str
+    country_code: str
+    asn_label: str
+    hits: int
+    hit_velocity: float
+    fuzzing_index: float
+    post_ratio: float
+    reason: str
+    client_headers: str = ""
+    asn_score: Optional[float] = None
+    asn_rep_label: str = ""
+    first_seen: Optional[datetime] = None
+    last_seen: Optional[datetime] = None
+    unique_ja4s: Optional[List[str]] = None
+    unique_hosts: Optional[List[str]] = None
+    anubis_bot_name: str = ""
+    anubis_bot_action: str = ""
+    anubis_bot_category: str = ""
+
+
+class DetectionsListResponse(BaseModel):
+    items: List[Detection]
+    total: int
+    page: int
+    page_size: int
+    total_pages: int
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# VARIABILITÉ
+# ─────────────────────────────────────────────────────────────────────────────
+
+class AttributeValue(BaseModel):
+    value: str
+    count: int
+    percentage: float
+    first_seen: Optional[datetime] = None
+    last_seen: Optional[datetime] = None
+    threat_levels: Optional[Dict[str, int]] = None
+    unique_ips: Optional[int] = None
+    primary_threat: Optional[str] = None
+
+
+class VariabilityAttributes(BaseModel):
+    user_agents: List[AttributeValue] = Field(default_factory=list)
+    ja4: List[AttributeValue] = Field(default_factory=list)
+    countries: List[AttributeValue] = Field(default_factory=list)
+    asns: List[AttributeValue] = Field(default_factory=list)
+    hosts: List[AttributeValue] = Field(default_factory=list)
+    threat_levels: List[AttributeValue] = Field(default_factory=list)
+    model_names: List[AttributeValue] = Field(default_factory=list)
+
+
+class Insight(BaseModel):
+    type: str  # "warning", "info", "success"
+    message: str
+
+
+class VariabilityResponse(BaseModel):
+    type: str
+    value: str
+    total_detections: int
+    unique_ips: int
+    date_range: Dict[str, datetime]
+    attributes: VariabilityAttributes
+    insights: List[Insight] = Field(default_factory=list)
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# ATTRIBUTS UNIQUES
+# ─────────────────────────────────────────────────────────────────────────────
+
+class AttributeListItem(BaseModel):
+    value: str
+    count: int
+
+
+class AttributeListResponse(BaseModel):
+    type: str
+    items: List[AttributeListItem]
+    total: int
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# USER-AGENTS
+# ─────────────────────────────────────────────────────────────────────────────
+
+class UserAgentValue(BaseModel):
+    value: str
+    count: int
+    percentage: float
+    first_seen: Optional[datetime] = None
+    last_seen: Optional[datetime] = None
+
+
+class UserAgentsResponse(BaseModel):
+    type: str
+    value: str
+    user_agents: List[UserAgentValue]
+    total: int
+    showing: int
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# CLASSIFICATIONS (SOC / ML)
+# ─────────────────────────────────────────────────────────────────────────────
+
+class ClassificationLabel(str, Enum):
+    LEGITIMATE = "legitimate"
+    SUSPICIOUS = "suspicious"
+    MALICIOUS = "malicious"
+
+
+class ClassificationBase(BaseModel):
+    ip: Optional[str] = None
+    ja4: Optional[str] = None
+    label: ClassificationLabel
+    tags: List[str] = Field(default_factory=list)
+    comment: str = ""
+    confidence: float = Field(ge=0.0, le=1.0, default=0.5)
+    analyst: str = "unknown"
+
+
+class ClassificationCreate(ClassificationBase):
+    """Données pour créer une classification"""
+    features: dict = Field(default_factory=dict)
+
+
+class Classification(ClassificationBase):
+    """Classification complète avec métadonnées"""
+    model_config = ConfigDict(from_attributes=True)
+
+    created_at: datetime
+    features: dict = Field(default_factory=dict)
+
+
+class ClassificationsListResponse(BaseModel):
+    items: List[Classification]
+    total: int
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# ANALYSIS (CORRELATION)
+# ─────────────────────────────────────────────────────────────────────────────
+
+class SubnetAnalysis(BaseModel):
+    """Analyse subnet/ASN"""
+    ip: str
+    subnet: str
+    ips_in_subnet: List[str]
+    total_in_subnet: int
+    asn_number: str
+    asn_org: str
+    total_in_asn: int
+    alert: bool  # True si > 10 IPs du subnet
+
+
+class CountryData(BaseModel):
+    """Données pour un pays"""
+    code: str
+    name: str
+    count: int
+    percentage: float
+
+
+class CountryAnalysis(BaseModel):
+    """Analyse des pays"""
+    top_countries: List[CountryData]
+    baseline: dict  # Pays habituels
+    alert_country: Optional[str] = None  # Pays surreprésenté
+
+
+class JA4SubnetData(BaseModel):
+    """Subnet pour un JA4"""
+    subnet: str
+    count: int
+
+
+class JA4Analysis(BaseModel):
+    """Analyse JA4"""
+    ja4: str
+    shared_ips_count: int
+    top_subnets: List[JA4SubnetData]
+    other_ja4_for_ip: List[str]
+
+
+class UserAgentData(BaseModel):
+    """Données pour un User-Agent"""
+    value: str
+    count: int
+    percentage: float
+    classification: str  # "normal", "bot", "script"
+
+
+class UserAgentAnalysis(BaseModel):
+    """Analyse User-Agents"""
+    ip_user_agents: List[UserAgentData]
+    ja4_user_agents: List[UserAgentData]
+    bot_percentage: float
+    alert: bool  # True si > 20% bots/scripts
+
+
+class CorrelationIndicators(BaseModel):
+    """Indicateurs de corrélation"""
+    subnet_ips_count: int
+    asn_ips_count: int
+    country_percentage: float
+    ja4_shared_ips: int
+    user_agents_count: int
+    bot_ua_percentage: float
+
+
+class ClassificationRecommendation(BaseModel):
+    """Recommandation de classification"""
+    label: ClassificationLabel
+    confidence: float
+    indicators: CorrelationIndicators
+    suggested_tags: List[str]
+    reason: str
+
+
+# ─────────────────────────────────────────────────────────────────────────────
+# ENTITIES (UNIFIED VIEW)
+# ─────────────────────────────────────────────────────────────────────────────
+
+class EntityStats(BaseModel):
+    """Statistiques pour une entité"""
+    entity_type: str
+    entity_value: str
+    total_requests: int
+    unique_ips: int
+    first_seen: datetime
+    last_seen: datetime
+
+
+class EntityRelatedAttributes(BaseModel):
+    """Attributs associés à une entité"""
+    ips: List[str] = Field(default_factory=list)
+    ja4s: List[str] = Field(default_factory=list)
+    hosts: List[str] = Field(default_factory=list)
+    asns: List[str] = Field(default_factory=list)
+    countries: List[str] = Field(default_factory=list)
+
+
+class EntityAttributeValue(BaseModel):
+    """Valeur d'attribut avec count et percentage (pour les entities)"""
+    value: str
+    count: int
+    percentage: float
+
+
+class EntityInvestigation(BaseModel):
+    """Investigation complète pour une entité"""
+    stats: EntityStats
+    related: EntityRelatedAttributes
+    user_agents: List[EntityAttributeValue] = Field(default_factory=list)
+    client_headers: List[EntityAttributeValue] = Field(default_factory=list)
+    paths: List[EntityAttributeValue] = Field(default_factory=list)
+    query_params: List[EntityAttributeValue] = Field(default_factory=list)

services/dashboard/backend/routes/__init__.py

@@ -0,0 +1 @@
+# Routes package

services/dashboard/backend/routes/analysis.py

@@ -0,0 +1,686 @@
+"""
+Endpoints pour l'analyse de corrélations et la classification SOC
+"""
+from collections import defaultdict
+from fastapi import APIRouter, HTTPException, Query
+from typing import Optional, List
+import ipaddress
+import json
+
+from ..database import db
+from ..models import (
+    SubnetAnalysis, CountryAnalysis, CountryData, JA4Analysis, JA4SubnetData,
+    UserAgentAnalysis, UserAgentData, CorrelationIndicators,
+    ClassificationRecommendation, ClassificationLabel,
+    ClassificationCreate, Classification, ClassificationsListResponse
+)
+
+router = APIRouter(prefix="/api/analysis", tags=["analysis"])
+
+# Mapping code ISO → nom lisible (utilisé par analyze_ip_country et analyze_country)
+_COUNTRY_NAMES: dict[str, str] = {
+    "CN": "China", "US": "United States", "DE": "Germany",
+    "FR": "France", "RU": "Russia", "GB": "United Kingdom",
+    "NL": "Netherlands", "IN": "India", "BR": "Brazil",
+    "JP": "Japan", "KR": "South Korea", "IT": "Italy",
+    "ES": "Spain", "CA": "Canada", "AU": "Australia"
+}
+
+# =============================================================================
+# ANALYSE SUBNET / ASN
+# =============================================================================
+
+@router.get("/{ip}/subnet", response_model=SubnetAnalysis)
+async def analyze_subnet(ip: str):
+    """
+    Analyse les IPs du même subnet et ASN
+    """
+    try:
+        # Calculer le subnet /24
+        ip_obj = ipaddress.ip_address(ip)
+        subnet = ipaddress.ip_network(f"{ip}/24", strict=False)
+        subnet_str = str(subnet)
+        
+        # Récupérer les infos ASN pour cette IP
+        asn_query = """
+        SELECT asn_number, asn_org
+        FROM ml_detected_anomalies
+        WHERE src_ip = %(ip)s
+        ORDER BY detected_at DESC
+        LIMIT 1
+        """
+        asn_result = db.query(asn_query, {"ip": ip})
+        
+        if not asn_result.result_rows:
+            # Fallback: utiliser données par défaut
+            asn_number = "0"
+            asn_org = "Unknown"
+        else:
+            asn_number = str(asn_result.result_rows[0][0] or "0")
+            asn_org = asn_result.result_rows[0][1] or "Unknown"
+        
+        # IPs du même subnet /24
+        subnet_ips_query = """
+        SELECT DISTINCT src_ip
+        FROM ml_detected_anomalies
+        WHERE toIPv4(src_ip) >= toIPv4(%(subnet_start)s)
+          AND toIPv4(src_ip) <= toIPv4(%(subnet_end)s)
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        ORDER BY src_ip
+        """
+        
+        subnet_result = db.query(subnet_ips_query, {
+            "subnet_start": str(subnet.network_address),
+            "subnet_end": str(subnet.broadcast_address)
+        })
+        
+        subnet_ips = [str(row[0]) for row in subnet_result.result_rows]
+        
+        # Total IPs du même ASN
+        if asn_number != "0":
+            asn_total_query = """
+            SELECT uniq(src_ip)
+            FROM ml_detected_anomalies
+            WHERE asn_number = %(asn_number)s
+              AND detected_at >= now() - INTERVAL 24 HOUR
+            """
+            
+            asn_total_result = db.query(asn_total_query, {"asn_number": asn_number})
+            asn_total = asn_total_result.result_rows[0][0] if asn_total_result.result_rows else 0
+        else:
+            asn_total = 0
+        
+        return SubnetAnalysis(
+            ip=ip,
+            subnet=subnet_str,
+            ips_in_subnet=subnet_ips,
+            total_in_subnet=len(subnet_ips),
+            asn_number=asn_number,
+            asn_org=asn_org,
+            total_in_asn=asn_total,
+            alert=len(subnet_ips) > 10
+        )
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+@router.get("/{ip}/country", response_model=dict)
+async def analyze_ip_country(ip: str):
+    """
+    Analyse le pays d'une IP spécifique et la répartition des autres pays du même ASN
+    """
+    try:
+        # Pays de l'IP
+        ip_country_query = """
+        SELECT country_code, asn_number
+        FROM ml_detected_anomalies
+        WHERE src_ip = %(ip)s
+        ORDER BY detected_at DESC
+        LIMIT 1
+        """
+        
+        ip_result = db.query(ip_country_query, {"ip": ip})
+        
+        if not ip_result.result_rows:
+            return {"ip_country": None, "asn_countries": []}
+        
+        ip_country_code = ip_result.result_rows[0][0]
+        asn_number = ip_result.result_rows[0][1]
+        
+        # Répartition des autres pays du même ASN
+        asn_countries_query = """
+        SELECT
+            country_code,
+            count() AS count
+        FROM ml_detected_anomalies
+        WHERE asn_number = %(asn_number)s
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        GROUP BY country_code
+        ORDER BY count DESC
+        """
+        
+        asn_result = db.query(asn_countries_query, {"asn_number": asn_number})
+        
+        total = sum(row[1] for row in asn_result.result_rows)
+        
+        asn_countries = [
+            {
+                "code": row[0],
+                "name": _COUNTRY_NAMES.get(row[0], row[0]),
+                "count": row[1],
+                "percentage": round((row[1] / total * 100), 2) if total > 0 else 0.0
+            }
+            for row in asn_result.result_rows
+        ]
+        
+        return {
+            "ip_country": {
+                "code": ip_country_code,
+                "name": _COUNTRY_NAMES.get(ip_country_code, ip_country_code)
+            },
+            "asn_countries": asn_countries
+        }
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+# =============================================================================
+# ANALYSE PAYS
+# =============================================================================
+
+@router.get("/country", response_model=CountryAnalysis)
+async def analyze_country(days: int = Query(1, ge=1, le=30)):
+    """
+    Analyse la distribution des pays
+    """
+    try:
+        # Top pays
+        top_query = """
+        SELECT
+            country_code,
+            count() AS count
+        FROM ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL %(days)s DAY
+          AND country_code != '' AND country_code IS NOT NULL
+        GROUP BY country_code
+        ORDER BY count DESC
+        """
+
+        top_result = db.query(top_query, {"days": days})
+        
+        # Calculer le total pour le pourcentage
+        total = sum(row[1] for row in top_result.result_rows)
+
+        top_countries = [
+            CountryData(
+                code=row[0],
+                name=_COUNTRY_NAMES.get(row[0], row[0]),
+                count=row[1],
+                percentage=round((row[1] / total * 100), 2) if total > 0 else 0.0
+            )
+            for row in top_result.result_rows
+        ]
+
+        # Baseline (7 derniers jours)
+        baseline_query = """
+        SELECT
+            country_code,
+            count() AS count
+        FROM ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL 7 DAY
+          AND country_code != '' AND country_code IS NOT NULL
+        GROUP BY country_code
+        ORDER BY count DESC
+        """
+        
+        baseline_result = db.query(baseline_query)
+        
+        baseline_total = sum(row[1] for row in baseline_result.result_rows)
+        baseline = {
+            row[0]: round((row[1] / baseline_total * 100), 2) if baseline_total > 0 else 0.0
+            for row in baseline_result.result_rows
+        }
+
+        # Détecter pays surreprésenté
+        alert_country = None
+        for country in top_countries:
+            baseline_pct = baseline.get(country.code, 0)
+            if baseline_pct > 0 and country.percentage > baseline_pct * 2 and country.percentage > 30:
+                alert_country = country.code
+                break
+        
+        return CountryAnalysis(
+            top_countries=top_countries,
+            baseline=baseline,
+            alert_country=alert_country
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+# =============================================================================
+# ANALYSE JA4
+# =============================================================================
+
+@router.get("/{ip}/ja4", response_model=JA4Analysis)
+async def analyze_ja4(ip: str):
+    """
+    Analyse le JA4 fingerprint
+    """
+    try:
+        # JA4 de cette IP
+        ja4_query = """
+        SELECT ja4
+        FROM ml_detected_anomalies
+        WHERE src_ip = %(ip)s
+          AND ja4 != '' AND ja4 IS NOT NULL
+        ORDER BY detected_at DESC
+        LIMIT 1
+        """
+        
+        ja4_result = db.query(ja4_query, {"ip": ip})
+        
+        if not ja4_result.result_rows:
+            return JA4Analysis(
+                ja4="",
+                shared_ips_count=0,
+                top_subnets=[],
+                other_ja4_for_ip=[]
+            )
+        
+        ja4 = ja4_result.result_rows[0][0]
+        
+        # IPs avec le même JA4
+        shared_query = """
+        SELECT uniq(src_ip)
+        FROM ml_detected_anomalies
+        WHERE ja4 = %(ja4)s
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        """
+        
+        shared_result = db.query(shared_query, {"ja4": ja4})
+        shared_count = shared_result.result_rows[0][0] if shared_result.result_rows else 0
+        
+        # Top subnets pour ce JA4 - Simplifié
+        subnets_query = """
+        SELECT
+            src_ip,
+            count() AS count
+        FROM ml_detected_anomalies
+        WHERE ja4 = %(ja4)s
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        GROUP BY src_ip
+        ORDER BY count DESC
+        """
+        
+        subnets_result = db.query(subnets_query, {"ja4": ja4})
+        
+        # Grouper par subnet /24
+        subnet_counts = defaultdict(int)
+        for row in subnets_result.result_rows:
+            ip_addr = str(row[0])
+            parts = ip_addr.split('.')
+            if len(parts) == 4:
+                subnet = f"{parts[0]}.{parts[1]}.{parts[2]}.0/24"
+                subnet_counts[subnet] += row[1]
+        
+        top_subnets = [
+            JA4SubnetData(subnet=subnet, count=count)
+            for subnet, count in sorted(subnet_counts.items(), key=lambda x: x[1], reverse=True)[:10]
+        ]
+        
+        # Autres JA4 pour cette IP
+        other_ja4_query = """
+        SELECT DISTINCT ja4
+        FROM ml_detected_anomalies
+        WHERE src_ip = %(ip)s
+          AND ja4 != '' AND ja4 IS NOT NULL
+          AND ja4 != %(current_ja4)s
+        """
+        
+        other_result = db.query(other_ja4_query, {"ip": ip, "current_ja4": ja4})
+        other_ja4 = [row[0] for row in other_result.result_rows]
+        
+        return JA4Analysis(
+            ja4=ja4,
+            shared_ips_count=shared_count,
+            top_subnets=top_subnets,
+            other_ja4_for_ip=other_ja4
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+# =============================================================================
+# ANALYSE USER-AGENTS
+# =============================================================================
+
+@router.get("/{ip}/user-agents", response_model=UserAgentAnalysis)
+async def analyze_user_agents(ip: str):
+    """
+    Analyse les User-Agents
+    """
+    try:
+        # User-Agents pour cette IP (depuis http_logs)
+        ip_ua_query = """
+        SELECT
+            header_user_agent AS ua,
+            count() AS count
+        FROM mabase_prod.http_logs
+        WHERE src_ip = %(ip)s
+          AND header_user_agent != '' AND header_user_agent IS NOT NULL
+          AND time >= now() - INTERVAL 24 HOUR
+        GROUP BY ua
+        ORDER BY count DESC
+        """
+        
+        # Total réel des requêtes (pour les pourcentages corrects)
+        ip_total_query = """
+        SELECT count()
+        FROM mabase_prod.http_logs
+        WHERE src_ip = %(ip)s
+          AND time >= now() - INTERVAL 24 HOUR
+        """
+        
+        ip_ua_result = db.query(ip_ua_query, {"ip": ip})
+        ip_total_result = db.query(ip_total_query, {"ip": ip})
+        
+        # Classification des UAs
+        def classify_ua(ua: str) -> str:
+            ua_lower = ua.lower()
+            if any(bot in ua_lower for bot in ['bot', 'crawler', 'spider', 'curl', 'wget', 'python', 'requests', 'scrapy']):
+                return 'bot'
+            if any(script in ua_lower for script in ['python', 'java', 'php', 'ruby', 'perl', 'node']):
+                return 'script'
+            if not ua or ua.strip() == '':
+                return 'script'
+            return 'normal'
+        
+        # Total réel de toutes les requêtes (pour des pourcentages corrects même avec LIMIT)
+        total_count = ip_total_result.result_rows[0][0] if ip_total_result.result_rows else 0
+        if total_count == 0:
+            total_count = sum(row[1] for row in ip_ua_result.result_rows)
+        
+        ip_user_agents = [
+            UserAgentData(
+                value=row[0],
+                count=row[1],
+                percentage=round((row[1] / total_count * 100), 2) if total_count > 0 else 0.0,
+                classification=classify_ua(row[0])
+            )
+            for row in ip_ua_result.result_rows
+        ]
+        
+        # Pour les UAs du JA4, on retourne les mêmes pour l'instant
+        ja4_user_agents = ip_user_agents
+        
+        # Pourcentage de bots
+        bot_count = sum(ua.count for ua in ip_user_agents if ua.classification in ['bot', 'script'])
+        bot_percentage = (bot_count / total_count * 100) if total_count > 0 else 0
+        
+        return UserAgentAnalysis(
+            ip_user_agents=ip_user_agents,
+            ja4_user_agents=ja4_user_agents,
+            bot_percentage=bot_percentage,
+            alert=bot_percentage > 20
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+# =============================================================================
+# RECOMMANDATION DE CLASSIFICATION
+# =============================================================================
+
+@router.get("/{ip}/recommendation", response_model=ClassificationRecommendation)
+async def get_classification_recommendation(ip: str):
+    """
+    Génère une recommandation de classification basée sur les corrélations
+    """
+    try:
+        # Récupérer les analyses
+        try:
+            subnet_analysis = await analyze_subnet(ip)
+        except Exception:
+            subnet_analysis = None
+
+        try:
+            country_analysis = await analyze_country(1)
+        except Exception:
+            country_analysis = None
+
+        try:
+            ja4_analysis = await analyze_ja4(ip)
+        except Exception:
+            ja4_analysis = None
+
+        try:
+            ua_analysis = await analyze_user_agents(ip)
+        except Exception:
+            ua_analysis = None
+
+        # Indicateurs par défaut
+        indicators = CorrelationIndicators(
+            subnet_ips_count=subnet_analysis.total_in_subnet if subnet_analysis else 0,
+            asn_ips_count=subnet_analysis.total_in_asn if subnet_analysis else 0,
+            country_percentage=0.0,
+            ja4_shared_ips=ja4_analysis.shared_ips_count if ja4_analysis else 0,
+            user_agents_count=len(ua_analysis.ja4_user_agents) if ua_analysis else 0,
+            bot_ua_percentage=ua_analysis.bot_percentage if ua_analysis else 0.0
+        )
+        
+        # Score de confiance
+        score = 0.0
+        reasons = []
+        tags = []
+        
+        # Subnet > 10 IPs
+        if subnet_analysis and subnet_analysis.total_in_subnet > 10:
+            score += 0.25
+            reasons.append(f"{subnet_analysis.total_in_subnet} IPs du même subnet")
+            tags.append("distributed")
+        
+        # JA4 partagé > 50 IPs
+        if ja4_analysis and ja4_analysis.shared_ips_count > 50:
+            score += 0.25
+            reasons.append(f"{ja4_analysis.shared_ips_count} IPs avec même JA4")
+            tags.append("ja4-rotation")
+        
+        # Bot UA > 20%
+        if ua_analysis and ua_analysis.bot_percentage > 20:
+            score += 0.25
+            reasons.append(f"{ua_analysis.bot_percentage:.0f}% UAs bots/scripts")
+            tags.append("bot-ua")
+        
+        # Pays surreprésenté
+        if country_analysis and country_analysis.alert_country:
+            score += 0.15
+            reasons.append(f"Pays {country_analysis.alert_country} surreprésenté")
+            tags.append(f"country-{country_analysis.alert_country.lower()}")
+        
+        # ASN hosting
+        if subnet_analysis:
+            hosting_keywords = ["ovh", "amazon", "aws", "google", "azure", "digitalocean", "linode", "vultr", "china169", "chinamobile"]
+            if any(kw in (subnet_analysis.asn_org or "").lower() for kw in hosting_keywords):
+                score += 0.10
+                tags.append("hosting-asn")
+        
+        # Déterminer label
+        if score >= 0.7:
+            label = ClassificationLabel.MALICIOUS
+            tags.append("campaign")
+        elif score >= 0.4:
+            label = ClassificationLabel.SUSPICIOUS
+        else:
+            label = ClassificationLabel.LEGITIMATE
+        
+        reason = " | ".join(reasons) if reasons else "Aucun indicateur fort"
+        
+        return ClassificationRecommendation(
+            label=label,
+            confidence=min(score, 1.0),
+            indicators=indicators,
+            suggested_tags=tags,
+            reason=reason
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+# =============================================================================
+# CLASSIFICATIONS CRUD
+# =============================================================================
+
+@router.post("/classifications", response_model=Classification)
+async def create_classification(data: ClassificationCreate):
+    """
+    Crée une classification pour une IP ou un JA4
+    """
+    try:
+        # Validation: soit ip, soit ja4 doit être fourni
+        if not data.ip and not data.ja4:
+            raise HTTPException(status_code=400, detail="IP ou JA4 requis")
+        
+        query = """
+        INSERT INTO mabase_prod.classifications
+        (ip, ja4, label, tags, comment, confidence, features, analyst, created_at)
+        VALUES
+        (%(ip)s, %(ja4)s, %(label)s, %(tags)s, %(comment)s, %(confidence)s, %(features)s, %(analyst)s, now())
+        """
+        
+        db.query(query, {
+            "ip": data.ip or "",
+            "ja4": data.ja4 or "",
+            "label": data.label.value,
+            "tags": data.tags,
+            "comment": data.comment,
+            "confidence": data.confidence,
+            "features": json.dumps(data.features),
+            "analyst": data.analyst
+        })
+        
+        # Récupérer la classification créée
+        where_clause = "ip = %(entity)s" if data.ip else "ja4 = %(entity)s"
+        select_query = f"""
+        SELECT ip, ja4, label, tags, comment, confidence, features, analyst, created_at
+        FROM mabase_prod.classifications
+        WHERE {where_clause}
+        ORDER BY created_at DESC
+        LIMIT 1
+        """
+        
+        result = db.query(select_query, {"entity": data.ip or data.ja4})
+        
+        if not result.result_rows:
+            raise HTTPException(status_code=404, detail="Classification non trouvée")
+        
+        row = result.result_rows[0]
+        return Classification(
+            ip=row[0] or None,
+            ja4=row[1] or None,
+            label=ClassificationLabel(row[2]),
+            tags=row[3],
+            comment=row[4],
+            confidence=row[5],
+            features=json.loads(row[6]) if row[6] else {},
+            analyst=row[7],
+            created_at=row[8]
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+@router.get("/classifications", response_model=ClassificationsListResponse)
+async def list_classifications(
+    ip: Optional[str] = Query(None, description="Filtrer par IP"),
+    ja4: Optional[str] = Query(None, description="Filtrer par JA4"),
+    label: Optional[str] = Query(None, description="Filtrer par label"),
+    limit: int = Query(100, ge=1, le=1000)
+):
+    """
+    Liste les classifications
+    """
+    try:
+        where_clauses = ["1=1"]
+        params = {"limit": limit}
+        
+        if ip:
+            where_clauses.append("ip = %(ip)s")
+            params["ip"] = ip
+        
+        if ja4:
+            where_clauses.append("ja4 = %(ja4)s")
+            params["ja4"] = ja4
+        
+        if label:
+            where_clauses.append("label = %(label)s")
+            params["label"] = label
+        
+        where_clause = " AND ".join(where_clauses)
+        
+        query = f"""
+        SELECT ip, ja4, label, tags, comment, confidence, features, analyst, created_at
+        FROM mabase_prod.classifications
+        WHERE {where_clause}
+        ORDER BY created_at DESC
+        LIMIT %(limit)s
+        """
+        
+        result = db.query(query, params)
+        
+        classifications = [
+            Classification(
+                ip=row[0] or None,
+                ja4=row[1] or None,
+                label=ClassificationLabel(row[2]),
+                tags=row[3],
+                comment=row[4],
+                confidence=row[5],
+                features=json.loads(row[6]) if row[6] else {},
+                analyst=row[7],
+                created_at=row[8]
+            )
+            for row in result.result_rows
+        ]
+        
+        # Total
+        count_query = f"""
+        SELECT count()
+        FROM mabase_prod.classifications
+        WHERE {where_clause}
+        """
+        
+        count_result = db.query(count_query, params)
+        total = count_result.result_rows[0][0] if count_result.result_rows else 0
+        
+        return ClassificationsListResponse(
+            items=classifications,
+            total=total
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+@router.get("/classifications/stats")
+async def get_classification_stats():
+    """
+    Statistiques des classifications
+    """
+    try:
+        stats_query = """
+        SELECT
+            label,
+            count() AS total,
+            uniq(ip) AS unique_ips,
+            avg(confidence) AS avg_confidence
+        FROM mabase_prod.classifications
+        GROUP BY label
+        ORDER BY total DESC
+        """
+        
+        result = db.query(stats_query)
+        
+        stats = [
+            {
+                "label": row[0],
+                "total": row[1],
+                "unique_ips": row[2],
+                "avg_confidence": float(row[3]) if row[3] else 0.0
+            }
+            for row in result.result_rows
+        ]
+        
+        return {"stats": stats}
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")

services/dashboard/backend/routes/attributes.py

@@ -0,0 +1,92 @@
+"""
+Endpoints pour la liste des attributs uniques
+"""
+from fastapi import APIRouter, HTTPException, Query
+from ..database import db
+from ..models import AttributeListResponse, AttributeListItem
+
+router = APIRouter(prefix="/api/attributes", tags=["attributes"])
+
+
+@router.get("/{attr_type}", response_model=AttributeListResponse)
+async def get_attributes(
+    attr_type: str,
+    limit: int = Query(100, ge=1, le=1000, description="Nombre maximum de résultats")
+):
+    """
+    Récupère la liste des valeurs uniques pour un type d'attribut
+    """
+    try:
+        # Mapping des types vers les colonnes
+        type_column_map = {
+            "ip": "src_ip",
+            "ja4": "ja4",
+            "country": "country_code",
+            "asn": "asn_number",
+            "host": "host",
+            "threat_level": "threat_level",
+            "model_name": "model_name",
+            "asn_org": "asn_org"
+        }
+        
+        if attr_type not in type_column_map:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Type invalide. Types supportés: {', '.join(type_column_map.keys())}"
+            )
+        
+        column = type_column_map[attr_type]
+        
+        # Requête de base
+        base_query = f"""
+        SELECT
+            {column} AS value,
+            count() AS count
+        FROM ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL 24 HOUR
+        """
+        
+        # Ajout du filtre pour exclure les valeurs vides/nulles
+        # Gestion spéciale pour les types IPv6/IPv4 qui ne peuvent pas être comparés à ''
+        if attr_type == "ip":
+            # Pour les adresses IP, on convertit en string et on filtre
+            query = f"""
+            SELECT value, count FROM (
+                SELECT toString({column}) AS value, count() AS count
+                FROM ml_detected_anomalies
+                WHERE detected_at >= now() - INTERVAL 24 HOUR
+                GROUP BY {column}
+            )
+            WHERE value != '' AND value IS NOT NULL
+            ORDER BY count DESC
+            LIMIT %(limit)s
+            """
+        else:
+            query = f"""
+            {base_query}
+              AND {column} != '' AND {column} IS NOT NULL
+            GROUP BY value
+            ORDER BY count DESC
+            LIMIT %(limit)s
+            """
+        
+        result = db.query(query, {"limit": limit})
+        
+        items = [
+            AttributeListItem(
+                value=str(row[0]),
+                count=row[1]
+            )
+            for row in result.result_rows
+        ]
+        
+        return AttributeListResponse(
+            type=attr_type,
+            items=items,
+            total=len(items)
+        )
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")

services/dashboard/backend/routes/audit.py

@@ -0,0 +1,238 @@
+"""
+Routes pour l'audit et les logs d'activité
+"""
+import logging
+from fastapi import APIRouter, HTTPException, Query, Request
+from typing import Optional
+from datetime import datetime
+from ..database import db
+
+router = APIRouter(prefix="/api/audit", tags=["audit"])
+logger = logging.getLogger(__name__)
+
+
+@router.post("/logs")
+async def create_audit_log(
+    request: Request,
+    action: str,
+    entity_type: Optional[str] = None,
+    entity_id: Optional[str] = None,
+    entity_count: Optional[int] = None,
+    details: Optional[dict] = None,
+    user: Optional[str] = "soc_user"
+):
+    """
+    Crée un log d'audit pour une action utilisateur
+    """
+    try:
+        # Récupérer l'IP du client
+        client_ip = request.client.host if request.client else "unknown"
+        
+        # Insérer dans ClickHouse
+        insert_query = """
+        INSERT INTO mabase_prod.audit_logs 
+        (timestamp, user_name, action, entity_type, entity_id, entity_count, details, client_ip)
+        VALUES 
+        (%(timestamp)s, %(user)s, %(action)s, %(entity_type)s, %(entity_id)s, %(entity_count)s, %(details)s, %(client_ip)s)
+        """
+        
+        params = {
+            'timestamp': datetime.now(),
+            'user': user,
+            'action': action,
+            'entity_type': entity_type,
+            'entity_id': entity_id,
+            'entity_count': entity_count,
+            'details': str(details) if details else '',
+            'client_ip': client_ip
+        }
+        
+        # Note: This requires the audit_logs table to exist
+        # See deploy_audit_logs_table.sql
+        try:
+            db.query(insert_query, params)
+        except Exception as e:
+            # La table peut ne pas encore exister — on logue mais on ne bloque pas l'appelant
+            logger.warning(f"Could not insert audit log: {e}")
+        
+        return {
+            "status": "success",
+            "message": "Audit log created",
+            "action": action,
+            "timestamp": params['timestamp'].isoformat()
+        }
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+@router.get("/logs")
+async def get_audit_logs(
+    hours: int = Query(24, ge=1, le=720, description="Fenêtre temporelle en heures"),
+    user: Optional[str] = Query(None, description="Filtrer par utilisateur"),
+    action: Optional[str] = Query(None, description="Filtrer par action"),
+    entity_type: Optional[str] = Query(None, description="Filtrer par type d'entité"),
+    limit: int = Query(100, ge=1, le=1000, description="Nombre maximum de résultats")
+):
+    """
+    Récupère les logs d'audit avec filtres
+    """
+    try:
+        where_clauses = ["timestamp >= now() - INTERVAL %(hours)s HOUR"]
+        params = {"hours": hours, "limit": limit}
+        
+        if user:
+            where_clauses.append("user_name = %(user)s")
+            params["user"] = user
+            
+        if action:
+            where_clauses.append("action = %(action)s")
+            params["action"] = action
+            
+        if entity_type:
+            where_clauses.append("entity_type = %(entity_type)s")
+            params["entity_type"] = entity_type
+        
+        where_clause = " AND ".join(where_clauses)
+        
+        query = f"""
+        SELECT
+            timestamp,
+            user_name,
+            action,
+            entity_type,
+            entity_id,
+            entity_count,
+            details,
+            client_ip
+        FROM mabase_prod.audit_logs
+        WHERE {where_clause}
+        ORDER BY timestamp DESC
+        LIMIT %(limit)s
+        """
+        
+        result = db.query(query, params)
+        
+        logs = []
+        for row in result.result_rows:
+            logs.append({
+                "timestamp": row[0].isoformat() if row[0] else "",
+                "user_name": row[1] or "",
+                "action": row[2] or "",
+                "entity_type": row[3] or "",
+                "entity_id": row[4] or "",
+                "entity_count": row[5] or 0,
+                "details": row[6] or "",
+                "client_ip": row[7] or ""
+            })
+        
+        return {
+            "items": logs,
+            "total": len(logs),
+            "period_hours": hours
+        }
+        
+    except Exception as e:
+        # If table doesn't exist, return empty result
+        if "Table" in str(e) and "doesn't exist" in str(e):
+            return {
+                "items": [],
+                "total": 0,
+                "period_hours": hours,
+                "warning": "Audit logs table not created yet"
+            }
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+@router.get("/stats")
+async def get_audit_stats(
+    hours: int = Query(24, ge=1, le=720)
+):
+    """
+    Statistiques d'audit
+    """
+    try:
+        query = """
+        SELECT
+            action,
+            count() AS count,
+            uniq(user_name) AS unique_users,
+            sum(entity_count) AS total_entities
+        FROM mabase_prod.audit_logs
+        WHERE timestamp >= now() - INTERVAL %(hours)s HOUR
+        GROUP BY action
+        ORDER BY count DESC
+        """
+        
+        result = db.query(query, {"hours": hours})
+        
+        stats = []
+        for row in result.result_rows:
+            stats.append({
+                "action": row[0] or "",
+                "count": row[1] or 0,
+                "unique_users": row[2] or 0,
+                "total_entities": row[3] or 0
+            })
+        
+        return {
+            "items": stats,
+            "period_hours": hours
+        }
+        
+    except Exception as e:
+        if "Table" in str(e) and "doesn't exist" in str(e):
+            return {
+                "items": [],
+                "period_hours": hours,
+                "warning": "Audit logs table not created yet"
+            }
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+@router.get("/users/activity")
+async def get_user_activity(
+    hours: int = Query(24, ge=1, le=720)
+):
+    """
+    Activité par utilisateur
+    """
+    try:
+        query = """
+        SELECT
+            user_name,
+            count() AS actions,
+            uniq(action) AS action_types,
+            min(timestamp) AS first_action,
+            max(timestamp) AS last_action
+        FROM mabase_prod.audit_logs
+        WHERE timestamp >= now() - INTERVAL %(hours)s HOUR
+        GROUP BY user_name
+        ORDER BY actions DESC
+        """
+        
+        result = db.query(query, {"hours": hours})
+        
+        users = []
+        for row in result.result_rows:
+            users.append({
+                "user_name": row[0] or "",
+                "actions": row[1] or 0,
+                "action_types": row[2] or 0,
+                "first_action": row[3].isoformat() if row[3] else "",
+                "last_action": row[4].isoformat() if row[4] else ""
+            })
+        
+        return {
+            "items": users,
+            "period_hours": hours
+        }
+        
+    except Exception as e:
+        if "Table" in str(e) and "doesn't exist" in str(e):
+            return {
+                "items": [],
+                "period_hours": hours,
+                "warning": "Audit logs table not created yet"
+            }
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")

services/dashboard/backend/routes/botnets.py

@@ -0,0 +1,105 @@
+"""
+Endpoints pour l'analyse des botnets via la propagation des fingerprints JA4
+"""
+from fastapi import APIRouter, HTTPException, Query
+
+from ..database import db
+
+router = APIRouter(prefix="/api/botnets", tags=["botnets"])
+
+
+def _botnet_class(unique_countries: int) -> str:
+    if unique_countries > 100:
+        return "global_botnet"
+    if unique_countries > 20:
+        return "regional_botnet"
+    return "concentrated"
+
+
+@router.get("/ja4-spread")
+async def get_ja4_spread():
+    """Propagation des JA4 fingerprints à travers les pays et les IPs."""
+    try:
+        sql = """
+        SELECT
+            ja4,
+            unique_ips,
+            unique_countries,
+            targeted_hosts
+        FROM mabase_prod.view_host_ja4_anomalies
+        ORDER BY unique_countries DESC
+        """
+        result = db.query(sql)
+        items = []
+        for row in result.result_rows:
+            ja4              = str(row[0])
+            unique_ips       = int(row[1])
+            unique_countries = int(row[2])
+            targeted_hosts   = int(row[3])
+            dist_score       = round(
+                unique_countries / max(unique_ips ** 0.5, 0.001), 2
+            )
+            items.append({
+                "ja4":               ja4,
+                "unique_ips":        unique_ips,
+                "unique_countries":  unique_countries,
+                "targeted_hosts":    targeted_hosts,
+                "distribution_score":dist_score,
+                "botnet_class":      _botnet_class(unique_countries),
+            })
+        return {"items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/ja4/{ja4}/countries")
+async def get_ja4_countries(ja4: str, limit: int = Query(30, ge=1, le=200)):
+    """Top pays pour un JA4 donné depuis agg_host_ip_ja4_1h."""
+    try:
+        sql = """
+        SELECT
+            src_country_code                                                        AS country_code,
+            uniq(replaceRegexpAll(toString(src_ip), '^::ffff:', ''))               AS unique_ips,
+            sum(hits)                                                               AS hits
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE ja4 = %(ja4)s
+        GROUP BY src_country_code
+        ORDER BY unique_ips DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"ja4": ja4, "limit": limit})
+        items = [
+            {
+                "country_code": str(row[0]),
+                "unique_ips":   int(row[1]),
+                "hits":         int(row[2]),
+            }
+            for row in result.result_rows
+        ]
+        return {"items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/summary")
+async def get_botnets_summary():
+    """Statistiques globales sur les botnets détectés."""
+    try:
+        sql = """
+        SELECT
+            countIf(unique_countries > 100)                         AS total_global_botnets,
+            sumIf(unique_ips, unique_countries > 50)                AS total_ips_in_botnets,
+            argMax(ja4, unique_countries)                           AS most_spread_ja4,
+            argMax(ja4, unique_ips)                                 AS most_ips_ja4
+        FROM mabase_prod.view_host_ja4_anomalies
+        """
+        result = db.query(sql)
+        row = result.result_rows[0]
+        return {
+            "total_global_botnets": int(row[0]),
+            "total_ips_in_botnets": int(row[1]),
+            "most_spread_ja4":      str(row[2]),
+            "most_ips_ja4":         str(row[3]),
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

services/dashboard/backend/routes/bruteforce.py

@@ -0,0 +1,141 @@
+"""
+Endpoints pour l'analyse des attaques par force brute sur les formulaires
+"""
+from fastapi import APIRouter, HTTPException, Query
+
+from ..database import db
+
+router = APIRouter(prefix="/api/bruteforce", tags=["bruteforce"])
+
+
+@router.get("/targets")
+async def get_bruteforce_targets():
+    """Liste des hôtes ciblés par brute-force, triés par total_hits DESC."""
+    try:
+        sql = """
+        SELECT
+            host,
+            uniq(src_ip)            AS unique_ips,
+            sum(hits)               AS total_hits,
+            sum(query_params_count) AS total_params,
+            groupArray(3)(ja4)      AS top_ja4s
+        FROM mabase_prod.view_form_bruteforce_detected
+        GROUP BY host
+        ORDER BY total_hits DESC
+        """
+        result = db.query(sql)
+        items = []
+        for row in result.result_rows:
+            host        = str(row[0])
+            unique_ips  = int(row[1])
+            total_hits  = int(row[2])
+            total_params= int(row[3])
+            top_ja4s    = [str(j) for j in (row[4] or [])]
+            attack_type = (
+                "credential_stuffing"
+                if total_hits > 0 and total_params / total_hits > 0.5
+                else "enumeration"
+            )
+            items.append({
+                "host":        host,
+                "unique_ips":  unique_ips,
+                "total_hits":  total_hits,
+                "total_params":total_params,
+                "attack_type": attack_type,
+                "top_ja4s":    top_ja4s,
+            })
+        return {"items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/attackers")
+async def get_bruteforce_attackers(limit: int = Query(50, ge=1, le=500)):
+    """Top IPs attaquantes triées par total_hits DESC."""
+    try:
+        sql = """
+        SELECT
+            replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS ip,
+            uniq(host)              AS distinct_hosts,
+            sum(hits)               AS total_hits,
+            sum(query_params_count) AS total_params,
+            argMax(ja4, hits)       AS ja4
+        FROM mabase_prod.view_form_bruteforce_detected
+        GROUP BY src_ip
+        ORDER BY total_hits DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"limit": limit})
+        items = []
+        for row in result.result_rows:
+            items.append({
+                "ip":            str(row[0]),
+                "distinct_hosts":int(row[1]),
+                "total_hits":    int(row[2]),
+                "total_params":  int(row[3]),
+                "ja4":           str(row[4]),
+            })
+        return {"items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/timeline")
+async def get_bruteforce_timeline():
+    """Hits par heure (dernières 72h) depuis agg_host_ip_ja4_1h."""
+    try:
+        sql = """
+        SELECT
+            toHour(window_start)                                        AS hour,
+            sum(hits)                                                   AS hits,
+            uniq(replaceRegexpAll(toString(src_ip), '^::ffff:', ''))   AS ips
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 72 HOUR
+        GROUP BY hour
+        ORDER BY hour ASC
+        """
+        result = db.query(sql)
+        hours = []
+        for row in result.result_rows:
+            hours.append({
+                "hour": int(row[0]),
+                "hits": int(row[1]),
+                "ips":  int(row[2]),
+            })
+        return {"hours": hours}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/host/{host:path}/attackers")
+async def get_host_attackers(host: str, limit: int = Query(20, ge=1, le=200)):
+    """Top IPs attaquant un hôte spécifique, avec JA4 et type d'attaque."""
+    try:
+        sql = """
+        SELECT
+            replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS ip,
+            sum(hits)               AS total_hits,
+            sum(query_params_count) AS total_params,
+            argMax(ja4, hits)       AS ja4,
+            max(hits)               AS max_hits_per_window
+        FROM mabase_prod.view_form_bruteforce_detected
+        WHERE host = %(host)s
+        GROUP BY src_ip
+        ORDER BY total_hits DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"host": host, "limit": limit})
+        items = []
+        for row in result.result_rows:
+            total_hits   = int(row[1])
+            total_params = int(row[2])
+            items.append({
+                "ip":          str(row[0]),
+                "total_hits":  total_hits,
+                "total_params":total_params,
+                "ja4":         str(row[3] or ""),
+                "attack_type": "credential_stuffing" if total_hits > 0 and total_params / total_hits > 0.5 else "enumeration",
+            })
+        return {"host": host, "items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

services/dashboard/backend/routes/clustering.py

@@ -0,0 +1,551 @@
+"""
+Clustering d'IPs multi-métriques — WebGL / deck.gl backend.
+
+- Calcul sur la TOTALITÉ des IPs (GROUP BY src_ip, ja4 sans LIMIT)
+- K-means++ vectorisé (numpy) + PCA-2D + enveloppes convexes (scipy)
+- Calcul en background thread + cache 30 min
+- Endpoints : /clusters, /status, /cluster/{id}/points
+"""
+import math
+import time
+import logging
+import threading
+from collections import Counter
+from concurrent.futures import ThreadPoolExecutor
+from typing import Any
+
+import numpy as np
+from fastapi import APIRouter, HTTPException, Query
+
+from ..database import db
+from ..services.clustering_engine import (
+    FEATURE_NAMES,
+    build_feature_vector, kmeans_pp, pca_2d, compute_hulls,
+    name_cluster, risk_score_from_centroid, standardize,
+    risk_to_gradient_color,
+)
+
+log = logging.getLogger(__name__)
+router = APIRouter(prefix="/api/clustering", tags=["clustering"])
+
+# ─── Cache global ──────────────────────────────────────────────────────────────
+_CACHE: dict[str, Any] = {
+    "status": "idle",        # idle | computing | ready | error
+    "error": None,
+    "result": None,          # dict résultat complet
+    "ts": 0.0,               # timestamp dernière mise à jour
+    "params": {},
+    "cluster_ips": {},       # cluster_idx → [(ip, ja4, pca_x, pca_y, risk)]
+}
+_CACHE_TTL = 1800  # 30 minutes
+_LOCK = threading.Lock()
+_EXECUTOR = ThreadPoolExecutor(max_workers=1, thread_name_prefix="clustering")
+
+# ─── Palette de couleurs (remplace l'ancienne logique menace) ─────────────────
+# Les couleurs sont désormais attribuées par index de cluster pour maximiser
+# la distinction visuelle, indépendamment du niveau de risque.
+
+
+# ─── SQL : TOUTES les IPs sans LIMIT ─────────────────────────────────────────
+_SQL_ALL_IPS = """
+SELECT
+    replaceRegexpAll(toString(t.src_ip), '^::ffff:', '') AS ip,
+    t.ja4,
+    any(t.tcp_ttl_raw)     AS ttl,
+    any(t.tcp_win_raw)     AS win,
+    any(t.tcp_scale_raw)   AS scale,
+    any(t.tcp_mss_raw)     AS mss,
+    any(t.first_ua)        AS ua,
+    sum(t.hits)            AS hits,
+
+    avg(abs(ml.anomaly_score))           AS avg_score,
+    avg(ml.hit_velocity)                 AS avg_velocity,
+    avg(ml.fuzzing_index)                AS avg_fuzzing,
+    avg(ml.is_headless)                  AS pct_headless,
+    avg(ml.post_ratio)                   AS avg_post,
+    avg(ml.ip_id_zero_ratio)             AS ip_id_zero,
+    avg(ml.temporal_entropy)             AS entropy,
+    avg(ml.modern_browser_score)         AS browser_score,
+    avg(ml.alpn_http_mismatch)           AS alpn_mismatch,
+    avg(ml.is_alpn_missing)              AS alpn_missing,
+    avg(ml.multiplexing_efficiency)      AS h2_eff,
+    avg(ml.header_order_confidence)      AS hdr_conf,
+    avg(ml.ua_ch_mismatch)               AS ua_ch_mismatch,
+    avg(ml.asset_ratio)                  AS asset_ratio,
+    avg(ml.direct_access_ratio)          AS direct_ratio,
+    avg(ml.distinct_ja4_count)           AS ja4_count,
+    max(ml.is_ua_rotating)              AS ua_rotating,
+
+    max(ml.threat_level)                 AS threat,
+    any(ml.country_code)                 AS country,
+    any(ml.asn_org)                      AS asn_org,
+
+    -- Features headers HTTP (depuis view_dashboard_entities)
+    avg(ml.has_accept_language)          AS hdr_accept_lang,
+    any(vh.hdr_enc)                      AS hdr_has_encoding,
+    any(vh.hdr_sec_fetch)                AS hdr_has_sec_fetch,
+    any(vh.hdr_count)                    AS hdr_count_raw,
+
+    -- Fingerprint HTTP Headers (depuis agg_header_fingerprint_1h + ml_detected_anomalies)
+    -- header_order_shared_count : nb d'IPs partageant le même fingerprint
+    --   → faible = fingerprint rare = comportement suspect
+    avg(ml.header_order_shared_count)    AS hfp_shared_count,
+    -- distinct_header_orders : nb de fingerprints distincts émis par cette IP
+    --   → élevé = rotation de fingerprint = comportement bot
+    avg(ml.distinct_header_orders)       AS hfp_distinct_orders,
+    -- Cookie et Referer issus de la table dédiée aux empreintes
+    any(hfp.hfp_cookie)                  AS hfp_cookie,
+    any(hfp.hfp_referer)                 AS hfp_referer
+FROM mabase_prod.agg_host_ip_ja4_1h t
+LEFT JOIN mabase_prod.ml_detected_anomalies ml
+    ON t.src_ip = ml.src_ip AND t.ja4 = ml.ja4
+   AND ml.detected_at >= now() - INTERVAL %(hours)s HOUR
+LEFT JOIN (
+    SELECT
+        toIPv6(concat('::ffff:', toString(src_ip))) AS src_ip_v6,
+        ja4,
+        any(arrayExists(x -> x LIKE '%%Accept-Encoding%%', client_headers)) AS hdr_enc,
+        any(arrayExists(x -> x LIKE '%%Sec-Fetch%%', client_headers))        AS hdr_sec_fetch,
+        any(length(splitByChar(',', client_headers[1])))                    AS hdr_count
+    FROM mabase_prod.view_dashboard_entities
+    WHERE length(client_headers) > 0
+      AND log_date >= today() - 2
+    GROUP BY src_ip_v6, ja4
+) vh ON t.src_ip = vh.src_ip_v6 AND t.ja4 = vh.ja4
+LEFT JOIN (
+    SELECT
+        src_ip,
+        avg(has_cookie)  AS hfp_cookie,
+        avg(has_referer) AS hfp_referer
+    FROM mabase_prod.agg_header_fingerprint_1h
+    WHERE window_start >= now() - INTERVAL %(hours)s HOUR
+    GROUP BY src_ip
+) hfp ON t.src_ip = hfp.src_ip
+WHERE t.window_start >= now() - INTERVAL %(hours)s HOUR
+  AND t.tcp_ttl_raw > 0
+GROUP BY t.src_ip, t.ja4
+"""
+
+_SQL_COLS = [
+    "ip", "ja4", "ttl", "win", "scale", "mss", "ua", "hits",
+    "avg_score", "avg_velocity", "avg_fuzzing", "pct_headless", "avg_post",
+    "ip_id_zero", "entropy", "browser_score", "alpn_mismatch", "alpn_missing",
+    "h2_eff", "hdr_conf", "ua_ch_mismatch", "asset_ratio", "direct_ratio",
+    "ja4_count", "ua_rotating", "threat", "country", "asn_org",
+    "hdr_accept_lang", "hdr_has_encoding", "hdr_has_sec_fetch", "hdr_count_raw",
+    "hfp_shared_count", "hfp_distinct_orders", "hfp_cookie", "hfp_referer",
+]
+
+
+# ─── Worker de clustering (thread pool) ──────────────────────────────────────
+
+def _run_clustering_job(k: int, hours: int, sensitivity: float = 1.0) -> None:
+    """Exécuté dans le thread pool. Met à jour _CACHE.
+
+    sensitivity : multiplicateur de k [0.5 – 5.0].
+      0.5 = vue très agrégée (k/2 clusters)
+      1.0 = comportement par défaut
+      2.0 = deux fois plus de clusters → groupes plus homogènes
+      5.0 = granularité maximale (classification la plus fine)
+
+    k_actual est plafonné à 300 pour éviter des temps de calcul excessifs.
+    n_init est réduit à 1 quand k_actual > 60 pour rester rapide.
+    """
+    k_actual = max(4, min(300, round(k * sensitivity)))
+    t0 = time.time()
+    with _LOCK:
+        _CACHE["status"] = "computing"
+        _CACHE["error"] = None
+
+    try:
+        log.info(f"[clustering] Démarrage k={k_actual} (base={k}×sens={sensitivity}) hours={hours}")
+
+        # ── 1. Chargement de toutes les IPs ──────────────────────────────
+        result = db.query(_SQL_ALL_IPS, {"hours": hours})
+        rows: list[dict] = []
+        for row in result.result_rows:
+            rows.append({col: row[i] for i, col in enumerate(_SQL_COLS)})
+
+        n = len(rows)
+        log.info(f"[clustering] {n} IPs chargées")
+        if n < k_actual:
+            raise ValueError(f"Seulement {n} IPs disponibles (k={k_actual} requis)")
+
+        # ── 2. Construction de la matrice de features (numpy) ────────────
+        X = np.array([build_feature_vector(r) for r in rows], dtype=np.float32)
+        log.info(f"[clustering] Matrice X: {X.shape} — {X.nbytes/1024/1024:.1f} MB")
+
+        # ── 3. Standardisation z-score ────────────────────────────────────
+        # Normalise par variance : features discriminantes (forte std)
+        # contribuent plus que les features quasi-constantes.
+        X64 = X.astype(np.float64)
+        X_std, feat_mean, feat_std = standardize(X64)
+
+        # ── 4. K-means++ sur l'espace standardisé ────────────────────────
+        # n_init réduit à 1 pour k élevé (> 60) afin de limiter le temps de calcul
+        n_init = 1 if k_actual > 60 else 3
+        km = kmeans_pp(X_std, k=k_actual, max_iter=80, n_init=n_init, seed=42)
+        log.info(f"[clustering] K-means: {km.n_iter} iters, inertia={km.inertia:.2f}")
+
+        # Centroïdes dans l'espace original [0,1] pour affichage radar
+        # (dé-standardisation : c_orig = c_std * std + mean, puis clip [0,1])
+        centroids_orig = np.clip(km.centroids * feat_std + feat_mean, 0.0, 1.0)
+
+        # ── 5. PCA-2D sur les features ORIGINALES (normalisées [0,1]) ────
+        coords = pca_2d(X64)   # (n, 2), normalisé [0,1]
+
+        # ── 5b. Enveloppes convexes par cluster ──────────────────────────
+        hulls = compute_hulls(coords, km.labels, k_actual)
+
+        # ── 6. Agrégation par cluster ─────────────────────────────────────
+        cluster_rows:    list[list[dict]] = [[] for _ in range(k_actual)]
+        cluster_coords:  list[list[list[float]]] = [[] for _ in range(k_actual)]
+        cluster_ips_map: dict[int, list] = {j: [] for j in range(k_actual)}
+
+        for i, label in enumerate(km.labels):
+            j = int(label)
+            cluster_rows[j].append(rows[i])
+            cluster_coords[j].append(coords[i].tolist())
+            cluster_ips_map[j].append((
+                rows[i]["ip"],
+                rows[i]["ja4"],
+                float(coords[i][0]),
+                float(coords[i][1]),
+                float(risk_score_from_centroid(centroids_orig[j])),
+            ))
+
+        # ── 7. Construction des nœuds ─────────────────────────────────────
+        nodes = []
+        for j in range(k_actual):
+            if not cluster_rows[j]:
+                continue
+
+            def avg_f(key: str, crows: list[dict] = cluster_rows[j]) -> float:
+                return float(np.mean([float(r.get(key) or 0) for r in crows]))
+
+            mean_ttl   = avg_f("ttl")
+            mean_mss   = avg_f("mss")
+            mean_scale = avg_f("scale")
+            mean_win   = avg_f("win")
+
+            raw_stats = {"mean_ttl": mean_ttl, "mean_mss": mean_mss, "mean_scale": mean_scale}
+            label_name = name_cluster(centroids_orig[j], raw_stats)
+            risk       = float(risk_score_from_centroid(centroids_orig[j]))
+            color      = risk_to_gradient_color(risk)
+
+            # Centroïde 2D = moyenne des coords du cluster
+            cxy = np.mean(cluster_coords[j], axis=0).tolist() if cluster_coords[j] else [0.5, 0.5]
+            ip_set   = list({r["ip"] for r in cluster_rows[j]})
+            ip_count = len(ip_set)
+            hit_count = int(sum(float(r.get("hits") or 0) for r in cluster_rows[j]))
+
+            threats   = [str(r.get("threat")  or "") for r in cluster_rows[j] if r.get("threat")]
+            countries = [str(r.get("country") or "") for r in cluster_rows[j] if r.get("country")]
+            orgs      = [str(r.get("asn_org") or "") for r in cluster_rows[j] if r.get("asn_org")]
+
+            def topk(lst: list[str], n: int = 5) -> list[str]:
+                return [v for v, _ in Counter(lst).most_common(n) if v]
+
+            radar = [
+                {"feature": name, "value": round(float(centroids_orig[j][i]), 4)}
+                for i, name in enumerate(FEATURE_NAMES)
+            ]
+
+            radius = max(8, min(30, int(math.log1p(ip_count) * 2.2)))
+
+            sample_rows = sorted(cluster_rows[j], key=lambda r: float(r.get("hits") or 0), reverse=True)[:8]
+            sample_ips  = [r["ip"] for r in sample_rows]
+            sample_ua   = str(cluster_rows[j][0].get("ua") or "")
+
+            nodes.append({
+                "id":           f"c{j}_k{k_actual}",
+                "cluster_idx":  j,
+                "label":        label_name,
+                "pca_x":        round(cxy[0], 6),
+                "pca_y":        round(cxy[1], 6),
+                "radius":       radius,
+                "color":        color,
+                "risk_score":   round(risk, 4),
+
+                "mean_ttl":     round(mean_ttl, 1),
+                "mean_mss":     round(mean_mss, 0),
+                "mean_scale":   round(mean_scale, 1),
+                "mean_win":     round(mean_win, 0),
+                "mean_velocity":round(avg_f("avg_velocity"),3),
+                "mean_fuzzing": round(avg_f("avg_fuzzing"), 3),
+                "mean_headless":round(avg_f("pct_headless"),3),
+                "mean_post":    round(avg_f("avg_post"),    3),
+                "mean_asset":   round(avg_f("asset_ratio"), 3),
+                "mean_direct":  round(avg_f("direct_ratio"),3),
+                "mean_alpn_mismatch": round(avg_f("alpn_mismatch"),3),
+                "mean_h2_eff":  round(avg_f("h2_eff"),     3),
+                "mean_hdr_conf":round(avg_f("hdr_conf"),   3),
+                "mean_ua_ch":   round(avg_f("ua_ch_mismatch"),3),
+                "mean_entropy": round(avg_f("entropy"),    3),
+                "mean_ja4_diversity": round(avg_f("ja4_count"),3),
+                "mean_ip_id_zero":    round(avg_f("ip_id_zero"),3),
+                "mean_browser_score": round(avg_f("browser_score"),1),
+                "mean_ua_rotating":   round(avg_f("ua_rotating"),3),
+
+                "ip_count":     ip_count,
+                "hit_count":    hit_count,
+                "top_threat":   topk(threats, 1)[0] if threats else "",
+                "top_countries":topk(countries, 5),
+                "top_orgs":     topk(orgs, 5),
+                "sample_ips":   sample_ips,
+                "sample_ua":    sample_ua,
+                "radar":        radar,
+
+                # Hull pour deck.gl PolygonLayer
+                "hull": hulls.get(j, []),
+            })
+
+        # ── 8. Arêtes k-NN entre clusters ────────────────────────────────
+        edges = []
+        seen: set[frozenset] = set()
+        for i, ni in enumerate(nodes):
+            ci = ni["cluster_idx"]
+            dists = sorted(
+                [(j, nj["cluster_idx"],
+                  float(np.sum((centroids_orig[ci] - centroids_orig[nj["cluster_idx"]]) ** 2)))
+                 for j, nj in enumerate(nodes) if j != i],
+                key=lambda x: x[2]
+            )
+            for j_idx, cj, d2 in dists[:2]:
+                key = frozenset([ni["id"], nodes[j_idx]["id"]])
+                if key in seen:
+                    continue
+                seen.add(key)
+                edges.append({
+                    "id":         f"e_{ni['id']}_{nodes[j_idx]['id']}",
+                    "source":     ni["id"],
+                    "target":     nodes[j_idx]["id"],
+                    "similarity": round(1.0 / (1.0 + math.sqrt(d2)), 3),
+                })
+
+        # ── 9. Stockage résultat + cache IPs ─────────────────────────────
+        total_ips  = sum(n_["ip_count"]  for n_ in nodes)
+        total_hits = sum(n_["hit_count"] for n_ in nodes)
+        elapsed    = round(time.time() - t0, 2)
+
+        result_dict = {
+            "nodes": nodes,
+            "edges": edges,
+            "stats": {
+                "total_clusters": len(nodes),
+                "total_ips":      total_ips,
+                "total_hits":     total_hits,
+                "n_samples":      n,
+                "k":              k_actual,
+                "k_base":         k,
+                "sensitivity":    sensitivity,
+                "elapsed_s":      elapsed,
+            },
+            "feature_names": FEATURE_NAMES,
+        }
+
+        with _LOCK:
+            _CACHE["result"]      = result_dict
+            _CACHE["cluster_ips"] = cluster_ips_map
+            _CACHE["status"]      = "ready"
+            _CACHE["ts"]          = time.time()
+            _CACHE["params"]      = {"k": k, "hours": hours, "sensitivity": sensitivity}
+            _CACHE["error"]       = None
+
+        log.info(f"[clustering] Terminé en {elapsed}s — {total_ips} IPs, {len(nodes)} clusters")
+
+    except Exception as e:
+        log.exception("[clustering] Erreur lors du calcul")
+        with _LOCK:
+            _CACHE["status"] = "error"
+            _CACHE["error"]  = str(e)
+
+
+def _maybe_trigger(k: int, hours: int, sensitivity: float) -> None:
+    """Lance le calcul si cache absent, expiré ou paramètres différents."""
+    with _LOCK:
+        status = _CACHE["status"]
+        params = _CACHE["params"]
+        ts     = _CACHE["ts"]
+
+    cache_stale = (time.time() - ts) > _CACHE_TTL
+    params_changed = (
+        params.get("k") != k or
+        params.get("hours") != hours or
+        params.get("sensitivity") != sensitivity
+    )
+
+    if status in ("computing",):
+        return  # déjà en cours
+
+    if status == "ready" and not cache_stale and not params_changed:
+        return  # cache frais
+
+    _EXECUTOR.submit(_run_clustering_job, k, hours, sensitivity)
+
+
+# ─── Endpoints ────────────────────────────────────────────────────────────────
+
+@router.get("/status")
+async def get_status():
+    """État du calcul en cours (polling frontend)."""
+    with _LOCK:
+        return {
+            "status":   _CACHE["status"],
+            "error":    _CACHE["error"],
+            "ts":       _CACHE["ts"],
+            "params":   _CACHE["params"],
+            "age_s":    round(time.time() - _CACHE["ts"], 0) if _CACHE["ts"] else None,
+        }
+
+
+@router.get("/clusters")
+async def get_clusters(
+    k:           int   = Query(20,  ge=4,   le=100, description="Nombre de clusters de base"),
+    hours:       int   = Query(24,  ge=1,   le=168, description="Fenêtre temporelle (heures)"),
+    sensitivity: float = Query(1.0, ge=0.5, le=5.0, description="Sensibilité : multiplicateur de k (5.0 = granularité maximale)"),
+    force:       bool  = Query(False, description="Forcer le recalcul"),
+):
+    """
+    Clustering multi-métriques sur TOUTES les IPs.
+
+    k_actual = round(k × sensitivity) — la sensibilité contrôle la granularité.
+    Retourne immédiatement depuis le cache. Déclenche le calcul si nécessaire.
+    """
+    if force:
+        with _LOCK:
+            _CACHE["status"]      = "idle"
+            _CACHE["ts"]          = 0.0
+            _CACHE["result"]      = None
+            _CACHE["cluster_ips"] = {}
+
+    _maybe_trigger(k, hours, sensitivity)
+
+    with _LOCK:
+        status = _CACHE["status"]
+        result = _CACHE["result"]
+        error  = _CACHE["error"]
+
+    if status == "computing":
+        return {"status": "computing", "message": "Calcul en cours, réessayez dans quelques secondes"}
+
+    if status == "error":
+        raise HTTPException(status_code=500, detail=error or "Erreur inconnue")
+
+    if result is None:
+        return {"status": "idle", "message": "Calcul démarré, réessayez dans quelques secondes"}
+
+    return {**result, "status": "ready"}
+
+
+@router.get("/cluster/{cluster_id}/points")
+async def get_cluster_points(
+    cluster_id: str,
+    limit:  int = Query(5000, ge=1, le=20000),
+    offset: int = Query(0, ge=0),
+):
+    """
+    Coordonnées PCA + métadonnées de toutes les IPs d'un cluster.
+    Utilisé par deck.gl ScatterplotLayer (drill-down ou zoom avancé).
+    """
+    with _LOCK:
+        status = _CACHE["status"]
+        ips_map = _CACHE["cluster_ips"]
+
+    if status != "ready" or not ips_map:
+        raise HTTPException(status_code=404, detail="Cache absent — appelez /clusters d'abord")
+
+    try:
+        idx = int(cluster_id.split("_")[0][1:])
+    except (ValueError, IndexError):
+        raise HTTPException(status_code=400, detail="cluster_id invalide (format: c{n}_k{k})")
+
+    members = ips_map.get(idx, [])
+    total   = len(members)
+    page    = members[offset: offset + limit]
+
+    points = [
+        {"ip": m[0], "ja4": m[1], "pca_x": round(m[2], 6), "pca_y": round(m[3], 6), "risk": round(m[4], 3)}
+        for m in page
+    ]
+    return {"points": points, "total": total, "offset": offset, "limit": limit}
+
+
+@router.get("/cluster/{cluster_id}/ips")
+async def get_cluster_ips(
+    cluster_id: str,
+    limit:  int = Query(100, ge=1, le=500),
+    offset: int = Query(0, ge=0),
+):
+    """IPs avec détails SQL (backward-compat avec l'ancienne UI)."""
+    with _LOCK:
+        status  = _CACHE["status"]
+        ips_map = _CACHE["cluster_ips"]
+
+    if status != "ready" or not ips_map:
+        raise HTTPException(status_code=404, detail="Cache absent — appelez /clusters d'abord")
+
+    try:
+        idx = int(cluster_id.split("_")[0][1:])
+    except (ValueError, IndexError):
+        raise HTTPException(status_code=400, detail="cluster_id invalide")
+
+    members = ips_map.get(idx, [])
+    total   = len(members)
+    page    = members[offset: offset + limit]
+    if not page:
+        return {"ips": [], "total": total, "cluster_id": cluster_id}
+
+    safe_ips  = [m[0].replace("'", "") for m in page[:200]]
+    ip_filter = ", ".join(f"'{ip}'" for ip in safe_ips)
+
+    sql = f"""
+    SELECT
+        replaceRegexpAll(toString(t.src_ip), '^::ffff:', '') AS src_ip,
+        t.ja4,
+        any(t.tcp_ttl_raw)                              AS ttl,
+        any(t.tcp_win_raw)                              AS win,
+        any(t.tcp_scale_raw)                            AS scale,
+        any(t.tcp_mss_raw)                              AS mss,
+        sum(t.hits)                                     AS hits,
+        any(t.first_ua)                                 AS ua,
+        round(avg(abs(ml.anomaly_score)), 3)            AS avg_score,
+        max(ml.threat_level)                            AS threat_level,
+        any(ml.country_code)                            AS country_code,
+        any(ml.asn_org)                                 AS asn_org,
+        round(avg(ml.fuzzing_index), 2)                 AS fuzzing,
+        round(avg(ml.hit_velocity), 2)                  AS velocity
+    FROM mabase_prod.agg_host_ip_ja4_1h t
+    LEFT JOIN mabase_prod.ml_detected_anomalies ml
+        ON t.src_ip = ml.src_ip AND t.ja4 = ml.ja4
+       AND ml.detected_at >= now() - INTERVAL 24 HOUR
+    WHERE t.window_start >= now() - INTERVAL 24 HOUR
+      AND replaceRegexpAll(toString(t.src_ip), '^::ffff:', '') IN ({ip_filter})
+    GROUP BY t.src_ip, t.ja4
+    ORDER BY hits DESC
+    """
+    try:
+        result = db.query(sql)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+    ips = []
+    for row in result.result_rows:
+        ips.append({
+            "ip":           str(row[0] or ""),
+            "ja4":          str(row[1] or ""),
+            "tcp_ttl":      int(row[2] or 0),
+            "tcp_win":      int(row[3] or 0),
+            "tcp_scale":    int(row[4] or 0),
+            "tcp_mss":      int(row[5] or 0),
+            "hits":         int(row[6] or 0),
+            "ua":           str(row[7] or ""),
+            "avg_score":    float(row[8] or 0),
+            "threat_level": str(row[9] or ""),
+            "country_code": str(row[10] or ""),
+            "asn_org":      str(row[11] or ""),
+            "fuzzing":      float(row[12] or 0),
+            "velocity":     float(row[13] or 0),
+        })
+
+    return {"ips": ips, "total": total, "cluster_id": cluster_id}

services/dashboard/backend/routes/detections.py

@@ -0,0 +1,450 @@
+"""
+Endpoints pour la liste des détections
+"""
+from fastapi import APIRouter, HTTPException, Query
+from typing import Optional, List
+from ..database import db
+from ..models import DetectionsListResponse, Detection
+
+router = APIRouter(prefix="/api/detections", tags=["detections"])
+
+# Mapping label ASN → score float (0 = très suspect, 1 = légitime)
+_ASN_LABEL_SCORES: dict[str, float] = {
+    'human': 0.9, 'bot': 0.05, 'proxy': 0.25, 'vpn': 0.3,
+    'tor': 0.1, 'datacenter': 0.4, 'scanner': 0.05, 'malicious': 0.05,
+}
+
+
+def _label_to_score(label: str) -> float | None:
+    """Convertit un label de réputation ASN en score numérique."""
+    if not label:
+        return None
+    return _ASN_LABEL_SCORES.get(label.lower(), 0.5)
+
+
+@router.get("", response_model=DetectionsListResponse, summary="Liste paginée des détections")
+async def get_detections(
+    page: int = Query(1, ge=1, description="Numéro de page"),
+    page_size: int = Query(25, ge=1, le=100, description="Nombre de lignes par page"),
+    threat_level: Optional[str] = Query(None, description="Filtrer par niveau de menace"),
+    model_name: Optional[str] = Query(None, description="Filtrer par modèle"),
+    country_code: Optional[str] = Query(None, description="Filtrer par pays"),
+    asn_number: Optional[str] = Query(None, description="Filtrer par ASN"),
+    search: Optional[str] = Query(None, description="Recherche texte (IP, JA4, Host)"),
+    sort_by: str = Query("detected_at", description="Trier par"),
+    sort_order: str = Query("DESC", description="Ordre (ASC/DESC)"),
+    group_by_ip: bool = Query(False, description="Grouper par IP (first_seen/last_seen agrégés)"),
+    score_type: Optional[str] = Query(None, description="Filtrer par type de score: BOT, REGLE, BOT_REGLE, SCORE")
+):
+    """
+    Récupère la liste des détections avec pagination et filtres
+    """
+    try:
+        # Construction de la requête
+        where_clauses = ["detected_at >= now() - INTERVAL 24 HOUR"]
+        params = {}
+        
+        if threat_level:
+            where_clauses.append("threat_level = %(threat_level)s")
+            params["threat_level"] = threat_level
+        
+        if model_name:
+            where_clauses.append("model_name = %(model_name)s")
+            params["model_name"] = model_name
+        
+        if country_code:
+            where_clauses.append("country_code = %(country_code)s")
+            params["country_code"] = country_code.upper()
+        
+        if asn_number:
+            where_clauses.append("asn_number = %(asn_number)s")
+            params["asn_number"] = asn_number
+        
+        if search:
+            where_clauses.append(
+                "(ilike(toString(src_ip), %(search)s) OR ilike(ja4, %(search)s) OR ilike(host, %(search)s))"
+            )
+            params["search"] = f"%{search}%"
+
+        if score_type:
+            st = score_type.upper()
+            if st == "BOT":
+                where_clauses.append("threat_level = 'KNOWN_BOT'")
+            elif st == "REGLE":
+                where_clauses.append("threat_level = 'ANUBIS_DENY'")
+            elif st == "BOT_REGLE":
+                where_clauses.append("threat_level IN ('KNOWN_BOT', 'ANUBIS_DENY')")
+            elif st == "SCORE":
+                where_clauses.append("threat_level NOT IN ('KNOWN_BOT', 'ANUBIS_DENY')")
+
+        where_clause = " AND ".join(where_clauses)
+        
+        # Requête de comptage
+        count_query = f"""
+        SELECT count()
+        FROM ml_detected_anomalies
+        WHERE {where_clause}
+        """
+        
+        count_result = db.query(count_query, params)
+        total = count_result.result_rows[0][0] if count_result.result_rows else 0
+        
+        # Requête principale
+        offset = (page - 1) * page_size
+        
+        sort_order = "DESC" if sort_order.upper() == "DESC" else "ASC"
+
+        # ── Mode groupé par IP (first_seen / last_seen depuis la DB) ────────────
+        if group_by_ip:
+            valid_sort_grouped = ["anomaly_score", "hits", "hit_velocity", "first_seen", "last_seen", "src_ip", "detected_at"]
+            grouped_sort = sort_by if sort_by in valid_sort_grouped else "last_seen"
+            # detected_at → last_seen (max(detected_at) dans le GROUP BY)
+            if grouped_sort == "detected_at":
+                grouped_sort = "last_seen"
+            # In outer query, min_score is exposed as anomaly_score — keep the alias
+            outer_sort = "min_score" if grouped_sort == "anomaly_score" else grouped_sort
+
+            # Count distinct IPs
+            count_ip_query = f"""
+            SELECT uniq(src_ip)
+            FROM ml_detected_anomalies
+            WHERE {where_clause}
+            """
+            cr = db.query(count_ip_query, params)
+            total = cr.result_rows[0][0] if cr.result_rows else 0
+
+            grouped_query = f"""
+            SELECT
+                ip_data.src_ip,
+                ip_data.first_seen,
+                ip_data.last_seen,
+                ip_data.detection_count,
+                ip_data.unique_ja4s,
+                ip_data.unique_hosts,
+                ip_data.min_score                                  AS anomaly_score,
+                ip_data.threat_level_best,
+                ip_data.model_name_best,
+                ip_data.country_code,
+                ip_data.asn_number,
+                ip_data.asn_org,
+                ip_data.hit_velocity,
+                ip_data.hits,
+                ip_data.asn_label,
+                ar.label                                          AS asn_rep_label,
+                ip_data.anubis_bot_name_best,
+                ip_data.anubis_bot_action_best,
+                ip_data.anubis_bot_category_best
+            FROM (
+                SELECT
+                    src_ip,
+                    min(detected_at)                              AS first_seen,
+                    max(detected_at)                              AS last_seen,
+                    count()                                       AS detection_count,
+                    groupUniqArray(5)(ja4)                        AS unique_ja4s,
+                    groupUniqArray(5)(host)                       AS unique_hosts,
+                    min(anomaly_score)                            AS min_score,
+                    argMin(threat_level,  anomaly_score)          AS threat_level_best,
+                    argMin(model_name,    anomaly_score)          AS model_name_best,
+                    any(country_code)                             AS country_code,
+                    any(asn_number)                               AS asn_number,
+                    any(asn_org)                                  AS asn_org,
+                    max(hit_velocity)                             AS hit_velocity,
+                    sum(hits)                                     AS hits,
+                    any(asn_label)                                AS asn_label,
+                    argMin(anubis_bot_name,     anomaly_score)    AS anubis_bot_name_best,
+                    argMin(anubis_bot_action,   anomaly_score)    AS anubis_bot_action_best,
+                    argMin(anubis_bot_category, anomaly_score)    AS anubis_bot_category_best
+                FROM ml_detected_anomalies
+                WHERE {where_clause}
+                GROUP BY src_ip
+            ) ip_data
+            LEFT JOIN mabase_prod.asn_reputation ar
+                   ON ar.src_asn = toUInt32OrZero(ip_data.asn_number)
+            ORDER BY {outer_sort} {sort_order}
+            LIMIT %(limit)s OFFSET %(offset)s
+            """
+            params["limit"] = page_size
+            params["offset"] = offset
+            gresult = db.query(grouped_query, params)
+
+            detections = []
+            for row in gresult.result_rows:
+                # row: src_ip, first_seen, last_seen, detection_count, unique_ja4s, unique_hosts,
+                #       anomaly_score, threat_level_best, model_name_best, country_code, asn_number,
+                #       asn_org, hit_velocity, hits, asn_label, asn_rep_label,
+                #       anubis_bot_name, anubis_bot_action, anubis_bot_category
+                ja4s  = list(row[4]) if row[4] else []
+                hosts = list(row[5]) if row[5] else []
+                detections.append(Detection(
+                    detected_at=row[1],
+                    src_ip=str(row[0]),
+                    ja4=ja4s[0] if ja4s else "",
+                    host=hosts[0] if hosts else "",
+                    bot_name="",
+                    anomaly_score=float(row[6]) if row[6] else 0.0,
+                    threat_level=row[7] or "LOW",
+                    model_name=row[8] or "",
+                    recurrence=int(row[3] or 0),
+                    asn_number=str(row[10]) if row[10] else "",
+                    asn_org=row[11] or "",
+                    asn_detail="",
+                    asn_domain="",
+                    country_code=row[9] or "",
+                    asn_label=row[14] or "",
+                    hits=int(row[13] or 0),
+                    hit_velocity=float(row[12]) if row[12] else 0.0,
+                    fuzzing_index=0.0,
+                    post_ratio=0.0,
+                    reason="",
+                    asn_rep_label=row[15] or "",
+                    asn_score=_label_to_score(row[15] or ""),
+                    first_seen=row[1],
+                    last_seen=row[2],
+                    unique_ja4s=ja4s,
+                    unique_hosts=hosts,
+                    anubis_bot_name=row[16] or "",
+                    anubis_bot_action=row[17] or "",
+                    anubis_bot_category=row[18] or "",
+                ))
+
+            total_pages = (total + page_size - 1) // page_size
+            return DetectionsListResponse(
+                items=detections, total=total, page=page,
+                page_size=page_size, total_pages=total_pages
+            )
+
+        # ── Mode individuel (comportement original) ──────────────────────────────
+        # Validation du tri
+        valid_sort_columns = [
+            "detected_at", "src_ip", "threat_level", "anomaly_score",
+            "asn_number", "country_code", "hits", "hit_velocity"
+        ]
+        if sort_by not in valid_sort_columns:
+            sort_by = "detected_at"
+        
+        main_query = f"""
+        SELECT
+            detected_at,
+            src_ip,
+            ja4,
+            host,
+            bot_name,
+            anomaly_score,
+            threat_level,
+            model_name,
+            recurrence,
+            asn_number,
+            asn_org,
+            asn_detail,
+            asn_domain,
+            country_code,
+            asn_label,
+            hits,
+            hit_velocity,
+            fuzzing_index,
+            post_ratio,
+            reason,
+            ar.label AS asn_rep_label,
+            anubis_bot_name,
+            anubis_bot_action,
+            anubis_bot_category
+        FROM ml_detected_anomalies
+        LEFT JOIN mabase_prod.asn_reputation ar ON ar.src_asn = toUInt32OrZero(asn_number)
+        WHERE {where_clause}
+        ORDER BY {sort_by} {sort_order}
+        LIMIT %(limit)s OFFSET %(offset)s
+        """
+        
+        params["limit"] = page_size
+        params["offset"] = offset
+        
+        result = db.query(main_query, params)
+
+        detections = [
+            Detection(
+                detected_at=row[0],
+                src_ip=str(row[1]),
+                ja4=row[2] or "",
+                host=row[3] or "",
+                bot_name=row[4] or "",
+                anomaly_score=float(row[5]) if row[5] else 0.0,
+                threat_level=row[6] or "LOW",
+                model_name=row[7] or "",
+                recurrence=row[8] or 0,
+                asn_number=str(row[9]) if row[9] else "",
+                asn_org=row[10] or "",
+                asn_detail=row[11] or "",
+                asn_domain=row[12] or "",
+                country_code=row[13] or "",
+                asn_label=row[14] or "",
+                hits=row[15] or 0,
+                hit_velocity=float(row[16]) if row[16] else 0.0,
+                fuzzing_index=float(row[17]) if row[17] else 0.0,
+                post_ratio=float(row[18]) if row[18] else 0.0,
+                reason=row[19] or "",
+                asn_rep_label=row[20] or "",
+                asn_score=_label_to_score(row[20] or ""),
+                anubis_bot_name=row[21] or "",
+                anubis_bot_action=row[22] or "",
+                anubis_bot_category=row[23] or "",
+            )
+            for row in result.result_rows
+        ]
+        
+        total_pages = (total + page_size - 1) // page_size
+        
+        return DetectionsListResponse(
+            items=detections,
+            total=total,
+            page=page,
+            page_size=page_size,
+            total_pages=total_pages
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur lors de la récupération des détections: {str(e)}")
+
+
+@router.get("/{detection_id}")
+async def get_detection_details(detection_id: str):
+    """
+    Récupère les détails d'une détection spécifique
+    detection_id peut être une IP ou un identifiant
+    """
+    try:
+        query = """
+        SELECT
+            detected_at,
+            src_ip,
+            ja4,
+            host,
+            bot_name,
+            anomaly_score,
+            threat_level,
+            model_name,
+            recurrence,
+            asn_number,
+            asn_org,
+            asn_detail,
+            asn_domain,
+            country_code,
+            asn_label,
+            hits,
+            hit_velocity,
+            fuzzing_index,
+            post_ratio,
+            port_exhaustion_ratio,
+            orphan_ratio,
+            tcp_jitter_variance,
+            tcp_shared_count,
+            true_window_size,
+            window_mss_ratio,
+            alpn_http_mismatch,
+            is_alpn_missing,
+            sni_host_mismatch,
+            header_count,
+            has_accept_language,
+            has_cookie,
+            has_referer,
+            modern_browser_score,
+            ua_ch_mismatch,
+            header_order_shared_count,
+            ip_id_zero_ratio,
+            request_size_variance,
+            multiplexing_efficiency,
+            mss_mobile_mismatch,
+            correlated,
+            reason,
+            asset_ratio,
+            direct_access_ratio,
+            is_ua_rotating,
+            distinct_ja4_count,
+            src_port_density,
+            ja4_asn_concentration,
+            ja4_country_concentration,
+            is_rare_ja4
+        FROM ml_detected_anomalies
+        WHERE src_ip = %(ip)s
+        ORDER BY detected_at DESC
+        LIMIT 1
+        """
+        
+        result = db.query(query, {"ip": detection_id})
+        
+        if not result.result_rows:
+            raise HTTPException(status_code=404, detail="Détection non trouvée")
+        
+        row = result.result_rows[0]
+        
+        return {
+            "detected_at": row[0],
+            "src_ip": str(row[1]),
+            "ja4": row[2] or "",
+            "host": row[3] or "",
+            "bot_name": row[4] or "",
+            "anomaly_score": float(row[5]) if row[5] else 0.0,
+            "threat_level": row[6] or "LOW",
+            "model_name": row[7] or "",
+            "recurrence": row[8] or 0,
+            "asn": {
+                "number": str(row[9]) if row[9] else "",
+                "org": row[10] or "",
+                "detail": row[11] or "",
+                "domain": row[12] or "",
+                "label": row[14] or ""
+            },
+            "country": {
+                "code": row[13] or "",
+            },
+            "metrics": {
+                "hits": row[15] or 0,
+                "hit_velocity": float(row[16]) if row[16] else 0.0,
+                "fuzzing_index": float(row[17]) if row[17] else 0.0,
+                "post_ratio": float(row[18]) if row[18] else 0.0,
+                "port_exhaustion_ratio": float(row[19]) if row[19] else 0.0,
+                "orphan_ratio": float(row[20]) if row[20] else 0.0,
+            },
+            "tcp": {
+                "jitter_variance": float(row[21]) if row[21] else 0.0,
+                "shared_count": row[22] or 0,
+                "true_window_size": row[23] or 0,
+                "window_mss_ratio": float(row[24]) if row[24] else 0.0,
+            },
+            "tls": {
+                "alpn_http_mismatch": bool(row[25]) if row[25] is not None else False,
+                "is_alpn_missing": bool(row[26]) if row[26] is not None else False,
+                "sni_host_mismatch": bool(row[27]) if row[27] is not None else False,
+            },
+            "headers": {
+                "count": row[28] or 0,
+                "has_accept_language": bool(row[29]) if row[29] is not None else False,
+                "has_cookie": bool(row[30]) if row[30] is not None else False,
+                "has_referer": bool(row[31]) if row[31] is not None else False,
+                "modern_browser_score": row[32] or 0,
+                "ua_ch_mismatch": bool(row[33]) if row[33] is not None else False,
+                "header_order_shared_count": row[34] or 0,
+            },
+            "behavior": {
+                "ip_id_zero_ratio": float(row[35]) if row[35] else 0.0,
+                "request_size_variance": float(row[36]) if row[36] else 0.0,
+                "multiplexing_efficiency": float(row[37]) if row[37] else 0.0,
+                "mss_mobile_mismatch": bool(row[38]) if row[38] is not None else False,
+                "correlated": bool(row[39]) if row[39] is not None else False,
+            },
+            "advanced": {
+                "asset_ratio": float(row[41]) if row[41] else 0.0,
+                "direct_access_ratio": float(row[42]) if row[42] else 0.0,
+                "is_ua_rotating": bool(row[43]) if row[43] is not None else False,
+                "distinct_ja4_count": row[44] or 0,
+                "src_port_density": float(row[45]) if row[45] else 0.0,
+                "ja4_asn_concentration": float(row[46]) if row[46] else 0.0,
+                "ja4_country_concentration": float(row[47]) if row[47] else 0.0,
+                "is_rare_ja4": bool(row[48]) if row[48] is not None else False,
+            },
+            "reason": row[40] or ""
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")

services/dashboard/backend/routes/entities.py

@@ -0,0 +1,509 @@
+"""
+Routes pour l'investigation d'entités (IP, JA4, User-Agent, Client-Header, Host, Path, Query-Param)
+"""
+from fastapi import APIRouter, HTTPException, Query
+from typing import Optional, List
+
+from ..database import db
+from ..models import (
+    EntityInvestigation,
+    EntityStats,
+    EntityRelatedAttributes,
+    EntityAttributeValue
+)
+
+router = APIRouter(prefix="/api/entities", tags=["Entities"])
+
+# Ensemble des types d'entités valides
+VALID_ENTITY_TYPES = frozenset({
+    'ip', 'ja4', 'user_agent', 'client_header', 'host', 'path', 'query_param'
+})
+
+
+def get_entity_stats(entity_type: str, entity_value: str, hours: int = 24) -> Optional[EntityStats]:
+    """
+    Récupère les statistiques pour une entité donnée
+    """
+    query = """
+    SELECT
+        entity_type,
+        entity_value,
+        sum(requests) as total_requests,
+        sum(unique_ips) as unique_ips,
+        min(log_date) as first_seen,
+        max(log_date) as last_seen
+    FROM mabase_prod.view_dashboard_entities
+    WHERE entity_type = %(entity_type)s
+      AND entity_value = %(entity_value)s
+      AND log_date >= toDate(now() - INTERVAL %(hours)s HOUR)
+    GROUP BY entity_type, entity_value
+    """
+    
+    result = db.query(query, {
+        'entity_type': entity_type,
+        'entity_value': entity_value,
+        'hours': hours
+    })
+    
+    if not result.result_rows:
+        return None
+    
+    row = result.result_rows[0]
+    return EntityStats(
+        entity_type=row[0],
+        entity_value=row[1],
+        total_requests=row[2],
+        unique_ips=row[3],
+        first_seen=row[4],
+        last_seen=row[5]
+    )
+
+
+def get_related_attributes(entity_type: str, entity_value: str, hours: int = 24) -> EntityRelatedAttributes:
+    """
+    Récupère les attributs associés à une entité
+    """
+    # Requête pour agréger tous les attributs associés
+    query = """
+    SELECT
+        (SELECT groupUniqArray(toString(src_ip)) FROM mabase_prod.view_dashboard_entities WHERE entity_type = %(entity_type)s AND entity_value = %(entity_value)s AND log_date >= toDate(now() - INTERVAL %(hours)s HOUR)) as ips,
+        (SELECT groupUniqArray(ja4) FROM mabase_prod.view_dashboard_entities WHERE entity_type = %(entity_type)s AND entity_value = %(entity_value)s AND log_date >= toDate(now() - INTERVAL %(hours)s HOUR) AND ja4 != '') as ja4s,
+        (SELECT groupUniqArray(host) FROM mabase_prod.view_dashboard_entities WHERE entity_type = %(entity_type)s AND entity_value = %(entity_value)s AND log_date >= toDate(now() - INTERVAL %(hours)s HOUR) AND host != '') as hosts,
+        (SELECT groupUniqArrayArray(asns) FROM mabase_prod.view_dashboard_entities WHERE entity_type = %(entity_type)s AND entity_value = %(entity_value)s AND log_date >= toDate(now() - INTERVAL %(hours)s HOUR) AND notEmpty(asns)) as asns,
+        (SELECT groupUniqArrayArray(countries) FROM mabase_prod.view_dashboard_entities WHERE entity_type = %(entity_type)s AND entity_value = %(entity_value)s AND log_date >= toDate(now() - INTERVAL %(hours)s HOUR) AND notEmpty(countries)) as countries
+    """
+    
+    result = db.query(query, {
+        'entity_type': entity_type,
+        'entity_value': entity_value,
+        'hours': hours
+    })
+
+    if not result.result_rows or not any(result.result_rows[0]):
+        return EntityRelatedAttributes(
+            ips=[],
+            ja4s=[],
+            hosts=[],
+            asns=[],
+            countries=[]
+        )
+
+    row = result.result_rows[0]
+    return EntityRelatedAttributes(
+        ips=[str(ip) for ip in (row[0] or []) if ip],
+        ja4s=[ja4 for ja4 in (row[1] or []) if ja4],
+        hosts=[host for host in (row[2] or []) if host],
+        asns=[asn for asn in (row[3] or []) if asn],
+        countries=[country for country in (row[4] or []) if country]
+    )
+
+
+def get_array_values(entity_type: str, entity_value: str, array_field: str, hours: int = 24) -> List[EntityAttributeValue]:
+    """
+    Extrait et retourne les valeurs d'un champ Array (user_agents, client_headers, etc.)
+    """
+    query = f"""
+    SELECT
+        value,
+        count() as count,
+        round(count * 100.0 / sum(count) OVER (), 2) as percentage
+    FROM (
+        SELECT
+            arrayJoin({array_field}) as value
+        FROM mabase_prod.view_dashboard_entities
+        WHERE entity_type = %(entity_type)s
+          AND entity_value = %(entity_value)s
+          AND log_date >= toDate(now() - INTERVAL %(hours)s HOUR)
+          AND notEmpty({array_field})
+    )
+    GROUP BY value
+    ORDER BY count DESC
+    """
+
+    result = db.query(query, {
+        'entity_type': entity_type,
+        'entity_value': entity_value,
+        'hours': hours
+    })
+
+    return [
+        EntityAttributeValue(
+            value=row[0],
+            count=row[1],
+            percentage=row[2]
+        )
+        for row in result.result_rows
+    ]
+
+
+@router.get("/subnet/{subnet:path}")
+async def get_subnet_investigation(
+    subnet: str,
+    hours: int = Query(default=24, ge=1, le=720)
+):
+    """
+    Récupère toutes les IPs d'un subnet /24 avec leurs statistiques
+    Utilise ml_detected_anomalies pour les détections + view_dashboard_entities pour les user-agents
+    """
+    try:
+        # Extraire l'IP de base du subnet (ex: 192.168.1.0/24 -> 192.168.1.0)
+        subnet_ip = subnet.replace('/24', '').replace('/16', '').replace('/8', '')
+
+        # Extraire les 3 premiers octets pour le filtre (ex: 141.98.11)
+        subnet_parts = subnet_ip.split('.')[:3]
+        subnet_prefix = subnet_parts[0]
+        subnet_mask = subnet_parts[1]
+        subnet_third = subnet_parts[2]
+        
+        # Stats globales du subnet - utilise ml_detected_anomalies + view_dashboard_entities pour UA
+        stats_query = """
+        WITH cleaned_ips AS (
+            SELECT
+                replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS clean_ip,
+                detected_at,
+                ja4,
+                host,
+                country_code,
+                asn_number
+            FROM ml_detected_anomalies
+            WHERE detected_at >= now() - INTERVAL %(hours)s HOUR
+        ),
+        subnet_filter AS (
+            SELECT *
+            FROM cleaned_ips
+            WHERE splitByChar('.', clean_ip)[1] = %(subnet_prefix)s
+              AND splitByChar('.', clean_ip)[2] = %(subnet_mask)s
+              AND splitByChar('.', clean_ip)[3] = %(subnet_third)s
+        ),
+        -- Récupérer les user-agents depuis view_dashboard_entities
+        ua_data AS (
+            SELECT
+                entity_value AS ip,
+                arrayJoin(user_agents) AS user_agent
+            FROM view_dashboard_entities
+            WHERE entity_type = 'ip'
+              AND log_date >= toDate(now() - INTERVAL %(hours)s HOUR)
+              AND splitByChar('.', entity_value)[1] = %(subnet_prefix)s
+              AND splitByChar('.', entity_value)[2] = %(subnet_mask)s
+              AND splitByChar('.', entity_value)[3] = %(subnet_third)s
+        )
+        SELECT
+            %(subnet)s AS subnet,
+            uniq(clean_ip) AS total_ips,
+            count() AS total_detections,
+            uniq(ja4) AS unique_ja4,
+            (SELECT uniq(user_agent) FROM ua_data) AS unique_ua,
+            uniq(host) AS unique_hosts,
+            argMax(country_code, detected_at) AS primary_country,
+            argMax(asn_number, detected_at) AS primary_asn,
+            min(detected_at) AS first_seen,
+            max(detected_at) AS last_seen
+        FROM subnet_filter
+        """
+        
+        stats_result = db.query(stats_query, {
+            "subnet": subnet,
+            "subnet_prefix": subnet_prefix,
+            "subnet_mask": subnet_mask,
+            "subnet_third": subnet_third,
+            "hours": hours
+        })
+
+        if not stats_result.result_rows or stats_result.result_rows[0][1] == 0:
+            raise HTTPException(status_code=404, detail="Subnet non trouvé")
+        
+        stats_row = stats_result.result_rows[0]
+        stats = {
+            "subnet": subnet,
+            "total_ips": stats_row[1] or 0,
+            "total_detections": stats_row[2] or 0,
+            "unique_ja4": stats_row[3] or 0,
+            "unique_ua": stats_row[4] or 0,
+            "unique_hosts": stats_row[5] or 0,
+            "primary_country": stats_row[6] or "XX",
+            "primary_asn": str(stats_row[7]) if stats_row[7] else "?",
+            "first_seen": stats_row[8].isoformat() if stats_row[8] else "",
+            "last_seen": stats_row[9].isoformat() if stats_row[9] else ""
+        }
+        
+        # Liste des IPs avec détails - 2 requêtes séparées + fusion en Python
+        ips_query = """
+        WITH cleaned_ips AS (
+            SELECT
+                replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS clean_ip,
+                detected_at,
+                ja4,
+                country_code,
+                asn_number,
+                threat_level,
+                anomaly_score
+            FROM ml_detected_anomalies
+            WHERE detected_at >= now() - INTERVAL %(hours)s HOUR
+        ),
+        subnet_filter AS (
+            SELECT *
+            FROM cleaned_ips
+            WHERE splitByChar('.', clean_ip)[1] = %(subnet_prefix)s
+              AND splitByChar('.', clean_ip)[2] = %(subnet_mask)s
+              AND splitByChar('.', clean_ip)[3] = %(subnet_third)s
+        )
+        SELECT
+            clean_ip AS ip,
+            count() AS total_detections,
+            uniq(ja4) AS unique_ja4,
+            argMax(country_code, detected_at) AS primary_country,
+            argMax(asn_number, detected_at) AS primary_asn,
+            argMax(threat_level, detected_at) AS threat_level,
+            avg(anomaly_score) AS avg_score,
+            min(detected_at) AS first_seen,
+            max(detected_at) AS last_seen
+        FROM subnet_filter
+        GROUP BY ip
+        ORDER BY total_detections DESC
+        """
+        
+        # Exécuter la première requête pour obtenir les IPs
+        ips_result = db.query(ips_query, {
+            "subnet_prefix": subnet_prefix,
+            "subnet_mask": subnet_mask,
+            "subnet_third": subnet_third,
+            "hours": hours
+        })
+        
+        # Extraire la liste des IPs pour la requête UA
+        ip_list = [str(row[0]) for row in ips_result.result_rows]
+        
+        # Requête pour les user-agents avec IN clause (utilise l'index)
+        unique_ua_dict = {}
+        if ip_list:
+            # Formater la liste pour la clause IN
+            ip_values = ', '.join(f"'{ip}'" for ip in ip_list)
+            ua_query = f"""
+            SELECT
+                entity_value AS ip,
+                uniq(arrayJoin(user_agents)) AS unique_ua
+            FROM view_dashboard_entities
+            PREWHERE entity_type = 'ip'
+            WHERE entity_value IN ({ip_values})
+              AND log_date >= today() - INTERVAL 30 DAY
+            GROUP BY entity_value
+            """
+            ua_result = db.query(ua_query, {})
+            unique_ua_dict = {row[0]: row[1] for row in ua_result.result_rows}
+        
+        # Fusionner les résultats
+        ips = []
+        for row in ips_result.result_rows:
+            ips.append({
+                "ip": str(row[0]),
+                "total_detections": row[1],
+                "unique_ja4": row[2],
+                "unique_ua": unique_ua_dict.get(row[0], 0),
+                "primary_country": row[3] or "XX",
+                "primary_asn": str(row[4]) if row[4] else "?",
+                "threat_level": row[5] or "LOW",
+                "avg_score": abs(row[6] or 0),
+                "first_seen": row[7].isoformat() if row[7] else "",
+                "last_seen": row[8].isoformat() if row[8] else ""
+            })
+        
+        return {
+            "stats": stats,
+            "ips": ips
+        }
+        
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+@router.get("/types")
+async def get_entity_types():
+    """
+    Retourne la liste des types d'entités supportés.
+    NOTE: Cette route DOIT être déclarée avant /{entity_type}/... pour ne pas être masquée.
+    """
+    return {
+        "entity_types": sorted(VALID_ENTITY_TYPES),
+        "descriptions": {
+            "ip": "Adresse IP source",
+            "ja4": "Fingerprint JA4 TLS",
+            "user_agent": "User-Agent HTTP",
+            "client_header": "Client Header",
+            "host": "Host HTTP",
+            "path": "Path URL",
+            "query_param": "Query Param"
+        }
+    }
+
+
+@router.get("/{entity_type}/{entity_value:path}", response_model=EntityInvestigation)
+async def get_entity_investigation(
+    entity_type: str,
+    entity_value: str,
+    hours: int = Query(default=24, ge=1, le=720, description="Fenêtre temporelle en heures")
+):
+    """
+    Investigation complète pour une entité donnée
+    
+    - **entity_type**: Type d'entité (ip, ja4, user_agent, client_header, host, path, query_param)
+    - **entity_value**: Valeur de l'entité
+    - **hours**: Fenêtre temporelle (défaut: 24h)
+    
+    Retourne:
+    - Stats générales
+    - Attributs associés (IPs, JA4, Hosts, ASNs, Pays)
+    - User-Agents
+    - Client-Headers
+    - Paths
+    - Query-Params
+    """
+    # Valider le type d'entité
+    if entity_type not in VALID_ENTITY_TYPES:
+        raise HTTPException(
+            status_code=400, 
+            detail=f"Type d'entité invalide. Types supportés: {', '.join(VALID_ENTITY_TYPES)}"
+        )
+    
+    # Stats générales
+    stats = get_entity_stats(entity_type, entity_value, hours)
+    if not stats:
+        raise HTTPException(status_code=404, detail="Entité non trouvée")
+    
+    # Attributs associés
+    related = get_related_attributes(entity_type, entity_value, hours)
+    
+    # User-Agents
+    user_agents = get_array_values(entity_type, entity_value, 'user_agents', hours)
+    
+    # Client-Headers
+    client_headers = get_array_values(entity_type, entity_value, 'client_headers', hours)
+    
+    # Paths
+    paths = get_array_values(entity_type, entity_value, 'paths', hours)
+    
+    # Query-Params
+    query_params = get_array_values(entity_type, entity_value, 'query_params', hours)
+    
+    return EntityInvestigation(
+        stats=stats,
+        related=related,
+        user_agents=user_agents,
+        client_headers=client_headers,
+        paths=paths,
+        query_params=query_params
+    )
+
+
+@router.get("/{entity_type}/{entity_value:path}/related")
+async def get_entity_related(
+    entity_type: str,
+    entity_value: str,
+    hours: int = Query(default=24, ge=1, le=720)
+):
+    """
+    Récupère uniquement les attributs associés à une entité
+    """
+    if entity_type not in VALID_ENTITY_TYPES:
+        raise HTTPException(
+            status_code=400, 
+            detail=f"Type d'entité invalide. Types supportés: {', '.join(VALID_ENTITY_TYPES)}"
+        )
+    
+    related = get_related_attributes(entity_type, entity_value, hours)
+    
+    return {
+        "entity_type": entity_type,
+        "entity_value": entity_value,
+        "hours": hours,
+        "related": related
+    }
+
+
+@router.get("/{entity_type}/{entity_value:path}/user_agents")
+async def get_entity_user_agents(
+    entity_type: str,
+    entity_value: str,
+    hours: int = Query(default=24, ge=1, le=720)
+):
+    """
+    Récupère les User-Agents associés à une entité
+    """
+    if entity_type not in VALID_ENTITY_TYPES:
+        raise HTTPException(status_code=400, detail="Type d'entité invalide")
+    
+    user_agents = get_array_values(entity_type, entity_value, 'user_agents', hours)
+    
+    return {
+        "entity_type": entity_type,
+        "entity_value": entity_value,
+        "user_agents": user_agents,
+        "total": len(user_agents)
+    }
+
+
+@router.get("/{entity_type}/{entity_value:path}/client_headers")
+async def get_entity_client_headers(
+    entity_type: str,
+    entity_value: str,
+    hours: int = Query(default=24, ge=1, le=720)
+):
+    """
+    Récupère les Client-Headers associés à une entité
+    """
+    if entity_type not in VALID_ENTITY_TYPES:
+        raise HTTPException(status_code=400, detail="Type d'entité invalide")
+    
+    client_headers = get_array_values(entity_type, entity_value, 'client_headers', hours)
+    
+    return {
+        "entity_type": entity_type,
+        "entity_value": entity_value,
+        "client_headers": client_headers,
+        "total": len(client_headers)
+    }
+
+
+@router.get("/{entity_type}/{entity_value:path}/paths")
+async def get_entity_paths(
+    entity_type: str,
+    entity_value: str,
+    hours: int = Query(default=24, ge=1, le=720)
+):
+    """
+    Récupère les Paths associés à une entité
+    """
+    if entity_type not in VALID_ENTITY_TYPES:
+        raise HTTPException(status_code=400, detail="Type d'entité invalide")
+    
+    paths = get_array_values(entity_type, entity_value, 'paths', hours)
+    
+    return {
+        "entity_type": entity_type,
+        "entity_value": entity_value,
+        "paths": paths,
+        "total": len(paths)
+    }
+
+
+@router.get("/{entity_type}/{entity_value:path}/query_params")
+async def get_entity_query_params(
+    entity_type: str,
+    entity_value: str,
+    hours: int = Query(default=24, ge=1, le=720)
+):
+    """
+    Récupère les Query-Params associés à une entité
+    """
+    if entity_type not in VALID_ENTITY_TYPES:
+        raise HTTPException(status_code=400, detail="Type d'entité invalide")
+    
+    query_params = get_array_values(entity_type, entity_value, 'query_params', hours)
+    
+    return {
+        "entity_type": entity_type,
+        "entity_value": entity_value,
+        "query_params": query_params,
+        "total": len(query_params)
+    }

services/dashboard/backend/routes/fingerprints.py

@@ -0,0 +1,827 @@
+"""
+Endpoints pour l'analyse des fingerprints JA4 et User-Agents
+
+Objectifs:
+  - Détecter le spoofing JA4 (fingerprint TLS qui prétend être un navigateur mais
+    dont les User-Agents, les headers HTTP ou les métriques comportementales trahissent
+    une origine bot/script)
+  - Construire une matrice JA4 × User-Agent pour visualiser les associations suspectes
+  - Analyser la distribution des User-Agents pour identifier les rotateurs et les bots
+    qui usurpent des UA de navigateurs légitimes
+"""
+from fastapi import APIRouter, HTTPException, Query
+import re
+
+from ..database import db
+
+router = APIRouter(prefix="/api/fingerprints", tags=["fingerprints"])
+
+
+# ─── Helpers ──────────────────────────────────────────────────────────────────
+
+# Patterns indiquant clairement un bot/script sans simulation de navigateur
+_BOT_PATTERNS = re.compile(
+    r"bot|crawler|spider|scraper|python|curl|wget|go-http|java/|axios|"
+    r"libwww|httpclient|okhttp|requests|aiohttp|httpx|playwright|puppeteer|"
+    r"selenium|headless|phantomjs",
+    re.IGNORECASE,
+)
+
+# Navigateurs légitimes communs — un JA4 de type "browser" devrait venir avec ces UAs
+_BROWSER_PATTERNS = re.compile(
+    r"mozilla|chrome|safari|firefox|edge|opera|trident",
+    re.IGNORECASE,
+)
+
+
+def _classify_ua(ua: str) -> str:
+    """Retourne 'bot', 'browser', ou 'script'"""
+    if not ua:
+        return "empty"
+    if _BOT_PATTERNS.search(ua):
+        return "bot"
+    if _BROWSER_PATTERNS.search(ua):
+        return "browser"
+    return "script"
+
+
+# =============================================================================
+# ENDPOINT 1 — Détection de spoofing JA4
+# =============================================================================
+
+@router.get("/spoofing")
+async def get_ja4_spoofing(
+    hours: int = Query(24, ge=1, le=168, description="Fenêtre temporelle"),
+    min_detections: int = Query(10, ge=1, description="Nombre minimum de détections"),
+    limit: int = Query(50, ge=1, le=200),
+):
+    """
+    Identifie les JA4 fingerprints suspects de spoofing navigateur.
+
+    Un JA4 est considéré suspect quand:
+      - Il présente un taux élevé de ua_ch_mismatch (header UA ≠ Client Hints)
+      - Son modern_browser_score est élevé mais les UAs associés sont des bots/scripts
+      - Il apparaît avec un taux élevé de sni_host_mismatch ou alpn_http_mismatch
+      - is_rare_ja4 = true avec un volume important
+
+    Retourne un score de confiance de spoofing [0-100] pour chaque JA4.
+    """
+    try:
+        # Agrégation par JA4 avec tous les indicateurs de spoofing
+        query = """
+        SELECT
+            ja4,
+            count() AS total_detections,
+            uniq(src_ip) AS unique_ips,
+
+            -- Indicateurs de mismatch
+            countIf(ua_ch_mismatch = true) AS ua_ch_mismatch_count,
+            round(countIf(ua_ch_mismatch = true) * 100.0 / count(), 2) AS ua_ch_mismatch_pct,
+            countIf(sni_host_mismatch = true) AS sni_mismatch_count,
+            round(countIf(sni_host_mismatch = true) * 100.0 / count(), 2) AS sni_mismatch_pct,
+            countIf(alpn_http_mismatch = true) AS alpn_mismatch_count,
+            round(countIf(alpn_http_mismatch = true) * 100.0 / count(), 2) AS alpn_mismatch_pct,
+
+            -- Indicateurs comportementaux
+            avg(modern_browser_score) AS avg_browser_score,
+            countIf(is_rare_ja4 = true) AS rare_ja4_count,
+            round(countIf(is_rare_ja4 = true) * 100.0 / count(), 2) AS rare_ja4_pct,
+            countIf(is_ua_rotating = true) AS ua_rotating_count,
+            round(countIf(is_ua_rotating = true) * 100.0 / count(), 2) AS ua_rotating_pct,
+
+            -- Métriques TLS/TCP
+            countIf(is_alpn_missing = true) AS alpn_missing_count,
+            avg(distinct_ja4_count) AS avg_distinct_ja4_per_ip,
+
+            -- Répartition threat levels
+            countIf(threat_level = 'CRITICAL') AS critical_count,
+            countIf(threat_level = 'HIGH') AS high_count,
+
+            -- Botnet indicators
+            avg(ja4_asn_concentration) AS avg_asn_concentration,
+            avg(ja4_country_concentration) AS avg_country_concentration,
+
+            argMax(threat_level, detected_at) AS last_threat_level
+        FROM ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL %(hours)s HOUR
+          AND ja4 != '' AND ja4 IS NOT NULL
+        GROUP BY ja4
+        HAVING total_detections >= %(min_detections)s
+        ORDER BY ua_ch_mismatch_pct DESC, total_detections DESC
+        LIMIT %(limit)s
+        """
+
+        result = db.query(query, {
+            "hours": hours,
+            "min_detections": min_detections,
+            "limit": limit,
+        })
+
+        # Fetch top UA per JA4 from view_dashboard_user_agents
+        ja4_list = [str(r[0]) for r in result.result_rows if r[0]]
+        ua_by_ja4: dict = {}
+        if ja4_list:
+            ja4_sql = ", ".join(f"'{j}'" for j in ja4_list[:100])
+            ua_q = f"""
+            SELECT ja4, groupArray(5)(ua) AS top_uas
+            FROM (
+                SELECT ja4, arrayJoin(user_agents) AS ua, sum(requests) AS cnt
+                FROM view_dashboard_user_agents
+                WHERE ja4 IN ({ja4_sql})
+                  AND hour >= now() - INTERVAL {hours} HOUR
+                  AND ua != ''
+                GROUP BY ja4, ua
+                ORDER BY ja4, cnt DESC
+            )
+            GROUP BY ja4
+            """
+            try:
+                ua_res = db.query(ua_q)
+                for ua_row in ua_res.result_rows:
+                    j4 = str(ua_row[0])
+                    if ua_row[1]:
+                        ua_by_ja4[j4] = list(ua_row[1])
+            except Exception:
+                pass
+
+        items = []
+        for row in result.result_rows:
+            ja4 = str(row[0])
+            ua_ch_mismatch_pct = float(row[4] or 0)
+            sni_mismatch_pct = float(row[6] or 0)
+            alpn_mismatch_pct = float(row[8] or 0)
+            avg_browser_score = float(row[9] or 0)
+            rare_ja4_pct = float(row[11] or 0)
+            ua_rotating_pct = float(row[13] or 0)
+            alpn_missing_count = int(row[14] or 0)
+            total = int(row[1] or 1)
+
+            top_uas = ua_by_ja4.get(ja4, [])
+            ua_classes = [_classify_ua(u) for u in top_uas]
+            has_bot_ua = any(c == "bot" for c in ua_classes)
+            has_browser_ua = any(c == "browser" for c in ua_classes)
+
+            # Spoofing confidence score [0-100]:
+            # UA/CH mismatch est le signal le plus fort (poids 40)
+            # Browser UA avec score navigateur élevé mais indicateurs bot (poids 25)
+            # SNI/ALPN mismatches (poids 15)
+            # is_rare_ja4 avec gros volume (poids 10)
+            # UA rotating (poids 10)
+            spoof_score = min(100, round(
+                ua_ch_mismatch_pct * 0.40
+                + (avg_browser_score * 25 / 100 if has_bot_ua else 0)
+                + sni_mismatch_pct * 0.10
+                + alpn_mismatch_pct * 0.05
+                + rare_ja4_pct * 0.10
+                + ua_rotating_pct * 0.10
+                + (10 if alpn_missing_count > total * 0.3 else 0)
+            ))
+
+            # Classification du JA4
+            if spoof_score >= 60:
+                classification = "spoofed_browser"
+            elif has_bot_ua and avg_browser_score < 30:
+                classification = "known_bot"
+            elif has_browser_ua and ua_ch_mismatch_pct < 10:
+                classification = "legitimate_browser"
+            else:
+                classification = "suspicious"
+
+            items.append({
+                "ja4": ja4,
+                "classification": classification,
+                "spoofing_score": spoof_score,
+                "total_detections": int(row[1] or 0),
+                "unique_ips": int(row[2] or 0),
+                "indicators": {
+                    "ua_ch_mismatch_pct": ua_ch_mismatch_pct,
+                    "sni_mismatch_pct": sni_mismatch_pct,
+                    "alpn_mismatch_pct": alpn_mismatch_pct,
+                    "avg_browser_score": round(avg_browser_score, 1),
+                    "rare_ja4_pct": rare_ja4_pct,
+                    "ua_rotating_pct": ua_rotating_pct,
+                    "alpn_missing_count": alpn_missing_count,
+                    "avg_asn_concentration": round(float(row[18] or 0), 3),
+                    "avg_country_concentration": round(float(row[19] or 0), 3),
+                },
+                "top_user_agents": [
+                    {"ua": u, "type": _classify_ua(u)} for u in top_uas
+                ],
+                "threat_breakdown": {
+                    "critical": int(row[16] or 0),
+                    "high": int(row[17] or 0),
+                    "last_level": str(row[20] or "LOW"),
+                },
+            })
+
+        # Trier: spoofed_browser d'abord, puis par score
+        items.sort(key=lambda x: (-x["spoofing_score"], -x["total_detections"]))
+
+        return {
+            "items": items,
+            "total": len(items),
+            "period_hours": hours,
+            "summary": {
+                "spoofed_browser": sum(1 for i in items if i["classification"] == "spoofed_browser"),
+                "known_bot": sum(1 for i in items if i["classification"] == "known_bot"),
+                "suspicious": sum(1 for i in items if i["classification"] == "suspicious"),
+                "legitimate_browser": sum(1 for i in items if i["classification"] == "legitimate_browser"),
+            },
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+# =============================================================================
+# ENDPOINT 2 — Matrice JA4 × User-Agent
+# =============================================================================
+
+@router.get("/ja4-ua-matrix")
+async def get_ja4_ua_matrix(
+    hours: int = Query(24, ge=1, le=168),
+    min_ips: int = Query(3, ge=1, description="Nombre minimum d'IPs pour inclure un JA4"),
+    limit: int = Query(30, ge=1, le=100),
+):
+    """
+    Matrice JA4 × User-Agent.
+
+    Pour chaque JA4:
+      - Top User-Agents associés (depuis view_dashboard_entities)
+      - Taux de ua_ch_mismatch
+      - Classification UA (bot / browser / script)
+      - Indicateur de spoofing si browser_score élevé + UA non-navigateur
+    """
+    try:
+        # Stats JA4 depuis ml_detected_anomalies
+        stats_query = """
+        SELECT
+            ja4,
+            uniq(src_ip) AS unique_ips,
+            count() AS total_detections,
+            round(countIf(ua_ch_mismatch = true) * 100.0 / count(), 2) AS ua_ch_mismatch_pct,
+            avg(modern_browser_score) AS avg_browser_score,
+            countIf(is_rare_ja4 = true) AS rare_count,
+            countIf(is_ua_rotating = true) AS rotating_count,
+            argMax(threat_level, detected_at) AS last_threat
+        FROM ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL %(hours)s HOUR
+          AND ja4 != '' AND ja4 IS NOT NULL
+        GROUP BY ja4
+        HAVING unique_ips >= %(min_ips)s
+        ORDER BY ua_ch_mismatch_pct DESC, unique_ips DESC
+        LIMIT %(limit)s
+        """
+
+        stats_res = db.query(stats_query, {"hours": hours, "min_ips": min_ips, "limit": limit})
+        ja4_list = [str(r[0]) for r in stats_res.result_rows]
+
+        if not ja4_list:
+            return {"items": [], "total": 0, "period_hours": hours}
+
+        # UAs par JA4 depuis view_dashboard_user_agents
+        ja4_sql = ", ".join(f"'{j}'" for j in ja4_list)
+        ua_query = f"""
+        SELECT
+            ja4,
+            ua,
+            sum(requests) AS cnt
+        FROM view_dashboard_user_agents
+        ARRAY JOIN user_agents AS ua
+        WHERE ja4 IN ({ja4_sql})
+          AND hour >= now() - INTERVAL {hours} HOUR
+          AND ua != ''
+        GROUP BY ja4, ua
+        ORDER BY ja4, cnt DESC
+        """
+
+        ua_by_ja4: dict = {}
+        try:
+            ua_res = db.query(ua_query)
+            for row in ua_res.result_rows:
+                j4 = str(row[0])
+                if j4 not in ua_by_ja4:
+                    ua_by_ja4[j4] = []
+                if len(ua_by_ja4[j4]) < 8:
+                    ua_by_ja4[j4].append({"ua": str(row[1]), "count": int(row[2] or 0)})
+        except Exception:
+            pass
+
+        items = []
+        for row in stats_res.result_rows:
+            ja4 = str(row[0])
+            unique_ips = int(row[1] or 0)
+            ua_ch_mismatch_pct = float(row[3] or 0)
+            avg_browser_score = float(row[4] or 0)
+
+            top_uas = ua_by_ja4.get(ja4, [])
+            ua_total = sum(u["count"] for u in top_uas) or 1
+
+            classified_uas = []
+            for u in top_uas:
+                ua_type = _classify_ua(u["ua"])
+                classified_uas.append({
+                    "ua": u["ua"],
+                    "count": u["count"],
+                    "pct": round(u["count"] * 100 / ua_total, 1),
+                    "type": ua_type,
+                })
+
+            bot_pct = sum(u["pct"] for u in classified_uas if u["type"] == "bot")
+            browser_pct = sum(u["pct"] for u in classified_uas if u["type"] == "browser")
+
+            # Spoofing flag: JA4 ressemble à un navigateur (browser_score élevé)
+            # mais les UAs sont des bots/scripts
+            is_spoofing = avg_browser_score > 50 and bot_pct > 30 and ua_ch_mismatch_pct > 20
+
+            items.append({
+                "ja4": ja4,
+                "unique_ips": unique_ips,
+                "total_detections": int(row[2] or 0),
+                "ua_ch_mismatch_pct": ua_ch_mismatch_pct,
+                "avg_browser_score": round(avg_browser_score, 1),
+                "rare_count": int(row[5] or 0),
+                "rotating_count": int(row[6] or 0),
+                "last_threat": str(row[7] or "LOW"),
+                "user_agents": classified_uas,
+                "ua_summary": {
+                    "bot_pct": round(bot_pct, 1),
+                    "browser_pct": round(browser_pct, 1),
+                    "script_pct": round(100 - bot_pct - browser_pct, 1),
+                    "total_distinct": len(top_uas),
+                },
+                "is_spoofing_suspect": is_spoofing,
+            })
+
+        return {
+            "items": items,
+            "total": len(items),
+            "period_hours": hours,
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+# =============================================================================
+# ENDPOINT 3 — Analyse globale des User-Agents
+# =============================================================================
+
+@router.get("/ua-analysis")
+async def get_ua_analysis(
+    hours: int = Query(24, ge=1, le=168),
+    limit: int = Query(50, ge=1, le=200),
+):
+    """
+    Analyse globale des User-Agents dans les détections.
+
+    Identifie:
+      - UAs de type bot/script
+      - UAs browser légitimes vs UAs browser utilisés par des bots (via ua_ch_mismatch)
+      - UAs rares/suspects qui tournent (is_ua_rotating)
+      - Distribution JA4 par UA pour détecter les UAs multi-fingerprints (rotation)
+    """
+    try:
+        # Top UAs globaux depuis view_dashboard_user_agents
+        ua_global_query = """
+        SELECT
+            ua,
+            sum(requests) AS ip_count
+        FROM view_dashboard_user_agents
+        ARRAY JOIN user_agents AS ua
+        WHERE hour >= now() - INTERVAL %(hours)s HOUR
+          AND ua != ''
+        GROUP BY ua
+        ORDER BY ip_count DESC
+        LIMIT %(limit)s
+        """
+
+        ua_global_res = db.query(ua_global_query, {"hours": hours, "limit": limit})
+        top_uas = [str(r[0]) for r in ua_global_res.result_rows]
+
+        # Pour chaque UA, chercher ses JA4 via view_dashboard_user_agents
+        ua_sql = ", ".join(f"'{u.replace(chr(39), chr(39)*2)}'" for u in top_uas[:50]) if top_uas else "''"
+        ja4_per_ua_query = f"""
+        SELECT
+            ua,
+            uniq(ja4) AS unique_ja4s,
+            groupUniqArray(3)(ja4) AS sample_ja4s
+        FROM view_dashboard_user_agents
+        ARRAY JOIN user_agents AS ua
+        WHERE ua IN ({ua_sql})
+          AND hour >= now() - INTERVAL {hours} HOUR
+          AND ua != ''
+          AND ja4 != ''
+        GROUP BY ua
+        """
+        ja4_by_ua: dict = {}
+        try:
+            ja4_res = db.query(ja4_per_ua_query)
+            for r in ja4_res.result_rows:
+                ja4_by_ua[str(r[0])] = {
+                    "unique_ja4s": int(r[1] or 0),
+                    "sample_ja4s": list(r[2] or []),
+                }
+        except Exception:
+            pass
+
+        # IPs avec is_ua_rotating depuis ml_detected_anomalies
+        rotating_query = """
+        SELECT
+            replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS clean_ip,
+            avg(ua_ch_mismatch) AS avg_ua_ch_mismatch
+        FROM ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL %(hours)s HOUR
+          AND is_ua_rotating = true
+        GROUP BY clean_ip
+        ORDER BY avg_ua_ch_mismatch DESC
+        """
+        rotating_ips: list = []
+        try:
+            rot_res = db.query(rotating_query, {"hours": hours})
+            rotating_ips = [str(r[0]) for r in rot_res.result_rows]
+        except Exception:
+            pass
+
+        # Construire la réponse
+        items = []
+        for row in ua_global_res.result_rows:
+            ua = str(row[0])
+            ip_count = int(row[1] or 0)
+            ua_type = _classify_ua(ua)
+            ja4_info = ja4_by_ua.get(ua, {"unique_ja4s": 0, "sample_ja4s": []})
+
+            # UA multi-JA4 est suspect: un vrai navigateur a généralement 1-2 JA4
+            multi_ja4_flag = ja4_info["unique_ja4s"] > 3
+
+            items.append({
+                "user_agent": ua,
+                "type": ua_type,
+                "ip_count": ip_count,
+                "unique_ja4_count": ja4_info["unique_ja4s"],
+                "sample_ja4s": ja4_info["sample_ja4s"],
+                "is_multi_ja4_suspect": multi_ja4_flag,
+                "risk_flags": _build_ua_risk_flags(ua, ua_type, ja4_info["unique_ja4s"], ip_count),
+            })
+
+        # IPs avec rotation d'UA
+        ua_rotating_stats = {
+            "rotating_ip_count": len(rotating_ips),
+            "sample_rotating_ips": rotating_ips[:10],
+        }
+
+        return {
+            "items": items,
+            "total": len(items),
+            "period_hours": hours,
+            "ua_rotating_stats": ua_rotating_stats,
+            "summary": {
+                "bot_count": sum(1 for i in items if i["type"] == "bot"),
+                "browser_count": sum(1 for i in items if i["type"] == "browser"),
+                "script_count": sum(1 for i in items if i["type"] == "script"),
+                "multi_ja4_suspect_count": sum(1 for i in items if i["is_multi_ja4_suspect"]),
+            },
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+def _build_ua_risk_flags(ua: str, ua_type: str, unique_ja4s: int, ip_count: int) -> list:
+    flags = []
+    if ua_type == "bot":
+        flags.append("ua_bot_signature")
+    elif ua_type == "script":
+        flags.append("ua_script_library")
+    if unique_ja4s > 5:
+        flags.append("ja4_rotation_suspect")
+    if unique_ja4s > 3 and ua_type == "browser":
+        flags.append("browser_ua_multi_fingerprint")
+    if ip_count > 100:
+        flags.append("high_volume")
+    return flags
+
+
+# =============================================================================
+# ENDPOINT 4 — JA4 d'un IP spécifique: analyse de cohérence UA/JA4
+# =============================================================================
+
+@router.get("/ip/{ip}/coherence")
+async def get_ip_fingerprint_coherence(ip: str):
+    """
+    Analyse la cohérence JA4/UA pour une IP spécifique.
+
+    Répond à la question: "Cette IP spoofait-elle son fingerprint?"
+
+    Calcule un score de cohérence basé sur:
+      - Correspondance entre JA4 (TLS client fingerprint) et User-Agent
+      - ua_ch_mismatch (User-Agent vs Client Hints)
+      - modern_browser_score vs type d'UA réel
+      - Nombre de JA4 distincts utilisés (rotation)
+      - sni_host_mismatch, alpn_http_mismatch
+    """
+    try:
+        # Données depuis ml_detected_anomalies
+        ml_query = """
+        SELECT
+            ja4,
+            ua_ch_mismatch,
+            modern_browser_score,
+            sni_host_mismatch,
+            alpn_http_mismatch,
+            is_alpn_missing,
+            is_rare_ja4,
+            is_ua_rotating,
+            distinct_ja4_count,
+            header_count,
+            has_accept_language,
+            has_cookie,
+            has_referer,
+            header_order_shared_count,
+            detected_at,
+            threat_level,
+            window_mss_ratio,
+            tcp_jitter_variance,
+            multiplexing_efficiency
+        FROM ml_detected_anomalies
+        WHERE src_ip = %(ip)s
+        ORDER BY detected_at DESC
+        """
+        ml_res = db.query(ml_query, {"ip": ip})
+
+        if not ml_res.result_rows:
+            raise HTTPException(status_code=404, detail="IP non trouvée dans les détections")
+
+        # User-agents réels depuis view_dashboard_user_agents
+        ua_query = """
+        SELECT ua, sum(requests) AS cnt
+        FROM view_dashboard_user_agents
+        ARRAY JOIN user_agents AS ua
+        WHERE toString(src_ip) = %(ip)s
+          AND hour >= now() - INTERVAL 72 HOUR
+          AND ua != ''
+        GROUP BY ua ORDER BY cnt DESC
+        """
+        ua_res = db.query(ua_query, {"ip": ip})
+        top_uas = [{"ua": str(r[0]), "count": int(r[1] or 0), "type": _classify_ua(str(r[0]))}
+                   for r in ua_res.result_rows]
+
+        # Agréger les indicateurs de la dernière session
+        rows = ml_res.result_rows
+        latest = rows[0]
+        total_rows = len(rows)
+
+        ua_ch_mismatch_count = sum(1 for r in rows if r[1])
+        sni_mismatch_count = sum(1 for r in rows if r[3])
+        alpn_mismatch_count = sum(1 for r in rows if r[4])
+        is_rare_count = sum(1 for r in rows if r[6])
+        is_rotating = any(r[7] for r in rows)
+        distinct_ja4s = {str(r[0]) for r in rows if r[0]}
+        avg_browser_score = sum(int(r[2] or 0) for r in rows) / total_rows
+
+        # UA analysis
+        has_browser_ua = any(u["type"] == "browser" for u in top_uas)
+        has_bot_ua = any(u["type"] == "bot" for u in top_uas)
+        primary_ua_type = top_uas[0]["type"] if top_uas else "empty"
+
+        # Calcul du score de spoofing
+        spoof_score = min(100, round(
+            (ua_ch_mismatch_count / total_rows * 100) * 0.40
+            + (avg_browser_score * 0.20 if has_bot_ua else 0)
+            + (sni_mismatch_count / total_rows * 100) * 0.10
+            + (alpn_mismatch_count / total_rows * 100) * 0.05
+            + (len(distinct_ja4s) * 5 if len(distinct_ja4s) > 2 else 0)
+            + (15 if is_rotating else 0)
+            + (10 if is_rare_count > total_rows * 0.5 else 0)
+        ))
+
+        # Verdict
+        if spoof_score >= 70:
+            verdict = "high_confidence_spoofing"
+        elif spoof_score >= 40:
+            verdict = "suspicious_spoofing"
+        elif has_bot_ua and avg_browser_score < 20:
+            verdict = "known_bot_no_spoofing"
+        elif has_browser_ua and spoof_score < 20:
+            verdict = "legitimate_browser"
+        else:
+            verdict = "inconclusive"
+
+        # Explication humaine
+        explanation = []
+        if ua_ch_mismatch_count > total_rows * 0.3:
+            explanation.append(f"UA-Client-Hints mismatch sur {round(ua_ch_mismatch_count*100/total_rows)}% des requêtes")
+        if has_bot_ua and avg_browser_score > 40:
+            explanation.append(f"JA4 ressemble à un navigateur (score {round(avg_browser_score)}/100) mais UA est de type bot")
+        if len(distinct_ja4s) > 2:
+            explanation.append(f"{len(distinct_ja4s)} JA4 distincts utilisés → rotation de fingerprint")
+        if is_rotating:
+            explanation.append("is_ua_rotating détecté → rotation d'User-Agent confirmée")
+        if sni_mismatch_count > 0:
+            explanation.append(f"SNI ≠ Host header sur {sni_mismatch_count}/{total_rows} requêtes")
+        if not explanation:
+            explanation.append("Aucun indicateur de spoofing majeur détecté")
+
+        return {
+            "ip": ip,
+            "verdict": verdict,
+            "spoofing_score": spoof_score,
+            "explanation": explanation,
+            "indicators": {
+                "ua_ch_mismatch_rate": round(ua_ch_mismatch_count / total_rows * 100, 1),
+                "sni_mismatch_rate": round(sni_mismatch_count / total_rows * 100, 1),
+                "alpn_mismatch_rate": round(alpn_mismatch_count / total_rows * 100, 1),
+                "avg_browser_score": round(avg_browser_score, 1),
+                "distinct_ja4_count": len(distinct_ja4s),
+                "is_ua_rotating": is_rotating,
+                "rare_ja4_rate": round(is_rare_count / total_rows * 100, 1),
+            },
+            "fingerprints": {
+                "ja4_list": list(distinct_ja4s),
+                "latest_ja4": str(latest[0] or ""),
+            },
+            "user_agents": top_uas,
+            "latest_detection": {
+                "detected_at": latest[14].isoformat() if latest[14] else "",
+                "threat_level": str(latest[15] or "LOW"),
+                "modern_browser_score": int(latest[2] or 0),
+                "header_count": int(latest[9] or 0),
+                "has_accept_language": bool(latest[10]),
+                "has_cookie": bool(latest[11]),
+                "has_referer": bool(latest[12]),
+                "header_order_shared_count": int(latest[13] or 0),
+            },
+        }
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+# =============================================================================
+# ENDPOINT 5 — JA4 légitimes (baseline / whitelist)
+# =============================================================================
+
+@router.get("/legitimate-ja4")
+async def get_legitimate_ja4(
+    hours: int = Query(168, ge=24, le=720, description="Fenêtre pour établir la baseline"),
+    min_ips: int = Query(50, ge=5, description="Nombre minimum d'IPs pour qualifier un JA4 de légitime"),
+):
+    """
+    Établit une baseline des JA4 fingerprints légitimes.
+
+    Un JA4 est considéré légitime si:
+      - Il est utilisé par un grand nombre d'IPs distinctes (> min_ips)
+      - Son taux de ua_ch_mismatch est faible (< 5%)
+      - Son modern_browser_score est élevé (> 60)
+      - Il n'est PAS is_rare_ja4
+      - Ses UAs sont dominés par des navigateurs connus
+
+    Utile comme whitelist pour réduire les faux positifs.
+    """
+    try:
+        query = """
+        SELECT
+            ja4,
+            uniq(src_ip) AS unique_ips,
+            count() AS total_detections,
+            round(countIf(ua_ch_mismatch = true) * 100.0 / count(), 2) AS ua_ch_mismatch_pct,
+            avg(modern_browser_score) AS avg_browser_score,
+            countIf(is_rare_ja4 = true) AS rare_count,
+            round(countIf(threat_level = 'CRITICAL') * 100.0 / count(), 2) AS critical_pct,
+            round(countIf(threat_level = 'HIGH') * 100.0 / count(), 2) AS high_pct
+        FROM ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL %(hours)s HOUR
+          AND ja4 != '' AND ja4 IS NOT NULL
+        GROUP BY ja4
+        HAVING unique_ips >= %(min_ips)s
+          AND ua_ch_mismatch_pct < 5.0
+          AND avg_browser_score > 60
+          AND rare_count = 0
+        ORDER BY unique_ips DESC
+        """
+
+        result = db.query(query, {"hours": hours, "min_ips": min_ips})
+
+        items = [
+            {
+                "ja4": str(row[0]),
+                "unique_ips": int(row[1] or 0),
+                "total_detections": int(row[2] or 0),
+                "ua_ch_mismatch_pct": float(row[3] or 0),
+                "avg_browser_score": round(float(row[4] or 0), 1),
+                "critical_pct": float(row[6] or 0),
+                "high_pct": float(row[7] or 0),
+                "legitimacy_confidence": min(100, round(
+                    (1 - float(row[3] or 0) / 100) * 40
+                    + float(row[4] or 0) * 0.40
+                    + min(int(row[1] or 0) / min_ips, 1) * 20
+                )),
+            }
+            for row in result.result_rows
+        ]
+
+        return {
+            "items": items,
+            "total": len(items),
+            "period_hours": hours,
+            "note": "Ces JA4 sont candidats à une whitelist. Vérifier manuellement avant de whitelister.",
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+# =============================================================================
+# ENDPOINT — Corrélation JA4 × ASN / Pays (C5)
+# Détecte les JA4 fortement concentrés sur un seul ASN ou pays
+# → signal de botnet ciblé ou d'infrastructure de test/attaque partagée
+# =============================================================================
+
+@router.get("/asn-correlation")
+async def get_ja4_asn_correlation(
+    min_concentration: float = Query(0.7, ge=0.0, le=1.0, description="Seuil min de concentration ASN ou pays"),
+    min_ips: int = Query(5, ge=1, description="Nombre minimum d'IPs par JA4"),
+    limit: int = Query(50, ge=1, le=200),
+):
+    """
+    Identifie les JA4 fingerprints fortement concentrés sur un seul ASN ou pays.
+    Un JA4 avec asn_concentration ≥ 0.7 signifie que ≥70% des IPs utilisant ce fingerprint
+    proviennent du même ASN → infrastructure de bot partagée ou datacenter suspect.
+    """
+    try:
+        # Two-pass: first aggregate per (ja4, asn) to get IP counts per ASN,
+        # then aggregate per ja4 to compute concentration ratio
+        sql = """
+        SELECT
+            ja4,
+            sum(ips_per_combo)                                          AS unique_ips,
+            uniq(src_asn)                                               AS unique_asns,
+            uniq(src_country_code)                                      AS unique_countries,
+            toString(argMax(src_asn, ips_per_combo))                    AS top_asn_number,
+            argMax(asn_name, ips_per_combo)                             AS top_asn_name,
+            argMax(src_country_code, country_ips)                       AS dominant_country,
+            sum(total_hits)                                             AS total_hits,
+            round(max(ips_per_combo) / greatest(sum(ips_per_combo), 1), 3) AS asn_concentration,
+            round(max(country_ips)   / greatest(sum(ips_per_combo), 1), 3) AS country_concentration
+        FROM (
+            SELECT
+                ja4,
+                src_asn,
+                src_country_code,
+                any(src_as_name) AS asn_name,
+                uniq(src_ip)     AS ips_per_combo,
+                uniq(src_ip)     AS country_ips,
+                sum(hits)        AS total_hits
+            FROM mabase_prod.agg_host_ip_ja4_1h
+            WHERE window_start >= now() - INTERVAL 24 HOUR
+              AND ja4 != ''
+            GROUP BY ja4, src_asn, src_country_code
+        )
+        GROUP BY ja4
+        HAVING unique_ips >= %(min_ips)s
+           AND (asn_concentration >= %(min_conc)s OR country_concentration >= %(min_conc)s)
+        ORDER BY asn_concentration DESC, unique_ips DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"min_ips": min_ips, "min_conc": min_concentration, "limit": limit})
+        items = []
+        for row in result.result_rows:
+            ja4                   = str(row[0])
+            unique_ips            = int(row[1])
+            unique_asns           = int(row[2])
+            unique_countries      = int(row[3])
+            top_asn_number        = str(row[4] or "")
+            top_asn_name          = str(row[5] or "")
+            dominant_country      = str(row[6] or "")
+            total_hits            = int(row[7] or 0)
+            asn_concentration     = float(row[8] or 0)
+            country_concentration = float(row[9] or 0)
+
+            if asn_concentration >= 0.85:
+                corr_type, risk = "asn_monopoly", "high"
+            elif asn_concentration >= min_concentration:
+                corr_type, risk = "asn_dominant", "medium"
+            elif country_concentration >= min_concentration:
+                corr_type, risk = "geo_targeted", "medium"
+            else:
+                corr_type, risk = "distributed", "low"
+
+            items.append({
+                "ja4":                  ja4,
+                "unique_ips":           unique_ips,
+                "unique_asns":          unique_asns,
+                "unique_countries":     unique_countries,
+                "top_asn_name":         top_asn_name,
+                "top_asn_number":       top_asn_number,
+                "dominant_country":     dominant_country,
+                "total_hits":           total_hits,
+                "asn_concentration":    asn_concentration,
+                "country_concentration":country_concentration,
+                "correlation_type":     corr_type,
+                "risk":                 risk,
+            })
+        return {"items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")

services/dashboard/backend/routes/header_fingerprint.py

@@ -0,0 +1,101 @@
+"""
+Endpoints pour l'analyse des empreintes d'en-têtes HTTP
+"""
+from fastapi import APIRouter, HTTPException, Query
+
+from ..database import db
+
+router = APIRouter(prefix="/api/headers", tags=["header_fingerprint"])
+
+
+@router.get("/clusters")
+async def get_header_clusters(limit: int = Query(50, ge=1, le=200)):
+    """Clusters d'empreintes d'en-têtes groupés par header_order_hash."""
+    try:
+        sql = """
+        SELECT
+            header_order_hash                                                       AS hash,
+            uniq(replaceRegexpAll(toString(src_ip), '^::ffff:', ''))               AS unique_ips,
+            avg(modern_browser_score)                                               AS avg_browser_score,
+            sum(ua_ch_mismatch)                                                     AS ua_ch_mismatch_count,
+            round(sum(ua_ch_mismatch) * 100.0 / count(), 2)                        AS ua_ch_mismatch_pct,
+            groupArray(5)(sec_fetch_mode)                                           AS top_sec_fetch_modes,
+            round(sum(has_cookie) * 100.0 / count(), 2)                            AS has_cookie_pct,
+            round(sum(has_referer) * 100.0 / count(), 2)                           AS has_referer_pct
+        FROM mabase_prod.agg_header_fingerprint_1h
+        GROUP BY header_order_hash
+        ORDER BY unique_ips DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"limit": limit})
+
+        total_sql = """
+        SELECT uniq(header_order_hash)
+        FROM mabase_prod.agg_header_fingerprint_1h
+        """
+        total_clusters = int(db.query(total_sql).result_rows[0][0])
+
+        clusters = []
+        for row in result.result_rows:
+            h                  = str(row[0])
+            unique_ips         = int(row[1])
+            avg_browser_score  = float(row[2] or 0)
+            ua_ch_mismatch_cnt = int(row[3])
+            ua_ch_mismatch_pct = float(row[4] or 0)
+            top_modes          = list(set(str(m) for m in (row[5] or [])))
+            has_cookie_pct     = float(row[6] or 0)
+            has_referer_pct    = float(row[7] or 0)
+
+            if avg_browser_score >= 90 and ua_ch_mismatch_pct < 5:
+                classification = "legitimate"
+            elif ua_ch_mismatch_pct > 50:
+                classification = "bot_suspicious"
+            else:
+                classification = "mixed"
+
+            clusters.append({
+                "hash":                h,
+                "unique_ips":          unique_ips,
+                "avg_browser_score":   round(avg_browser_score, 2),
+                "ua_ch_mismatch_count":ua_ch_mismatch_cnt,
+                "ua_ch_mismatch_pct":  ua_ch_mismatch_pct,
+                "top_sec_fetch_modes": top_modes,
+                "has_cookie_pct":      has_cookie_pct,
+                "has_referer_pct":     has_referer_pct,
+                "classification":      classification,
+            })
+        return {"clusters": clusters, "total_clusters": total_clusters}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/cluster/{hash}/ips")
+async def get_cluster_ips(hash: str, limit: int = Query(50, ge=1, le=500)):
+    """Liste des IPs appartenant à un cluster d'en-têtes donné."""
+    try:
+        sql = """
+        SELECT
+            replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS ip,
+            any(modern_browser_score)                          AS browser_score,
+            any(ua_ch_mismatch)                                AS ua_ch_mismatch,
+            any(sec_fetch_mode)                                AS sec_fetch_mode,
+            any(sec_fetch_dest)                                AS sec_fetch_dest
+        FROM mabase_prod.agg_header_fingerprint_1h
+        WHERE header_order_hash = %(hash)s
+        GROUP BY src_ip
+        ORDER BY browser_score DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"hash": hash, "limit": limit})
+        items = []
+        for row in result.result_rows:
+            items.append({
+                "ip":             str(row[0]),
+                "browser_score":  int(row[1] or 0),
+                "ua_ch_mismatch": int(row[2] or 0),
+                "sec_fetch_mode": str(row[3] or ""),
+                "sec_fetch_dest": str(row[4] or ""),
+            })
+        return {"items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

services/dashboard/backend/routes/heatmap.py

@@ -0,0 +1,144 @@
+"""
+Endpoints pour la heatmap temporelle (hits par heure / hôte)
+"""
+from collections import defaultdict
+from fastapi import APIRouter, HTTPException, Query
+
+from ..database import db
+
+router = APIRouter(prefix="/api/heatmap", tags=["heatmap"])
+
+
+@router.get("/hourly")
+async def get_heatmap_hourly():
+    """Hits agrégés par heure sur les 72 dernières heures."""
+    try:
+        sql = """
+        SELECT
+            toHour(window_start)                                                    AS hour,
+            sum(hits)                                                               AS hits,
+            uniq(replaceRegexpAll(toString(src_ip), '^::ffff:', ''))               AS unique_ips,
+            max(max_requests_per_sec)                                               AS max_rps
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 72 HOUR
+        GROUP BY hour
+        ORDER BY hour ASC
+        """
+        result = db.query(sql)
+        hours = [
+            {
+                "hour":       int(row[0]),
+                "hits":       int(row[1]),
+                "unique_ips": int(row[2]),
+                "max_rps":    int(row[3]),
+            }
+            for row in result.result_rows
+        ]
+        return {"hours": hours}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/top-hosts")
+async def get_heatmap_top_hosts(limit: int = Query(20, ge=1, le=100)):
+    """Hôtes les plus ciblés avec répartition horaire sur 24h."""
+    try:
+        # Aggregate overall stats per host
+        agg_sql = """
+        SELECT
+            host,
+            sum(hits)                                                               AS total_hits,
+            uniq(replaceRegexpAll(toString(src_ip), '^::ffff:', ''))               AS unique_ips,
+            uniq(ja4)                                                               AS unique_ja4s
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 72 HOUR
+        GROUP BY host
+        ORDER BY total_hits DESC
+        LIMIT %(limit)s
+        """
+        agg_res = db.query(agg_sql, {"limit": limit})
+        top_hosts = [str(r[0]) for r in agg_res.result_rows]
+        host_stats = {
+            str(r[0]): {
+                "host":       str(r[0]),
+                "total_hits": int(r[1]),
+                "unique_ips": int(r[2]),
+                "unique_ja4s":int(r[3]),
+            }
+            for r in agg_res.result_rows
+        }
+
+        if not top_hosts:
+            return {"items": []}
+
+        # Hourly breakdown per host
+        hourly_sql = """
+        SELECT
+            host,
+            toHour(window_start) AS hour,
+            sum(hits)            AS hits
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 72 HOUR
+          AND host IN %(hosts)s
+        GROUP BY host, hour
+        """
+        hourly_res = db.query(hourly_sql, {"hosts": top_hosts})
+
+        hourly_map: dict = defaultdict(lambda: [0] * 24)
+        for row in hourly_res.result_rows:
+            h    = str(row[0])
+            hour = int(row[1])
+            hits = int(row[2])
+            hourly_map[h][hour] += hits
+
+        items = []
+        for host in top_hosts:
+            entry = dict(host_stats[host])
+            entry["hourly_hits"] = hourly_map[host]
+            items.append(entry)
+
+        return {"items": items}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/matrix")
+async def get_heatmap_matrix():
+    """Matrice top-15 hôtes × 24 heures (sum hits) sur les 72 dernières heures."""
+    try:
+        top_sql = """
+        SELECT host, sum(hits) AS total_hits
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 72 HOUR
+        GROUP BY host
+        ORDER BY total_hits DESC
+        """
+        top_res   = db.query(top_sql)
+        top_hosts = [str(r[0]) for r in top_res.result_rows]
+
+        if not top_hosts:
+            return {"hosts": [], "matrix": []}
+
+        cell_sql = """
+        SELECT
+            host,
+            toHour(window_start) AS hour,
+            sum(hits)            AS hits
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 72 HOUR
+          AND host IN %(hosts)s
+        GROUP BY host, hour
+        """
+        cell_res = db.query(cell_sql, {"hosts": top_hosts})
+
+        matrix_map: dict = defaultdict(lambda: [0] * 24)
+        for row in cell_res.result_rows:
+            h    = str(row[0])
+            hour = int(row[1])
+            hits = int(row[2])
+            matrix_map[h][hour] += hits
+
+        matrix = [matrix_map[h] for h in top_hosts]
+        return {"hosts": top_hosts, "matrix": matrix}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

services/dashboard/backend/routes/incidents.py

@@ -0,0 +1,266 @@
+"""
+Routes pour la gestion des incidents clusterisés
+"""
+import hashlib
+from fastapi import APIRouter, HTTPException, Query
+from typing import List, Optional
+from datetime import datetime
+from ..database import db
+
+router = APIRouter(prefix="/api/incidents", tags=["incidents"])
+
+
+@router.get("/clusters")
+async def get_incident_clusters(
+    hours: int = Query(24, ge=1, le=168, description="Fenêtre temporelle en heures"),
+    min_severity: str = Query("LOW", description="Niveau de sévérité minimum"),
+    limit: int = Query(20, ge=1, le=100, description="Nombre maximum de clusters")
+):
+    """
+    Récupère les incidents clusterisés automatiquement
+    
+    Les clusters sont formés par:
+    - Subnet /24
+    - JA4 fingerprint
+    - Pattern temporel
+    """
+    try:
+        # Cluster par subnet /24 avec une IP exemple
+        # Note: src_ip est en IPv6, les IPv4 sont stockés comme ::ffff:x.x.x.x
+        # toIPv4() convertit les IPv4-mapped, IPv4NumToString() retourne l'IPv4 en notation x.x.x.x
+        cluster_query = """
+        WITH cleaned_ips AS (
+            SELECT
+                replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS clean_ip,
+                detected_at,
+                ja4,
+                country_code,
+                asn_number,
+                threat_level,
+                anomaly_score
+            FROM ml_detected_anomalies
+            WHERE detected_at >= now() - INTERVAL %(hours)s HOUR
+        ),
+        subnet_groups AS (
+            SELECT
+                concat(
+                    splitByChar('.', clean_ip)[1], '.',
+                    splitByChar('.', clean_ip)[2], '.',
+                    splitByChar('.', clean_ip)[3], '.0/24'
+                ) AS subnet,
+                count() AS total_detections,
+                uniq(clean_ip) AS unique_ips,
+                min(detected_at) AS first_seen,
+                max(detected_at) AS last_seen,
+                argMax(ja4, detected_at) AS ja4,
+                argMax(country_code, detected_at) AS country_code,
+                argMax(asn_number, detected_at) AS asn_number,
+                argMax(threat_level, detected_at) AS threat_level,
+                avg(anomaly_score) AS avg_score,
+                argMax(clean_ip, detected_at) AS sample_ip
+            FROM cleaned_ips
+            GROUP BY subnet
+            HAVING total_detections >= 2
+        )
+        SELECT
+            subnet,
+            total_detections,
+            unique_ips,
+            first_seen,
+            last_seen,
+            ja4,
+            country_code,
+            asn_number,
+            threat_level,
+            avg_score,
+            sample_ip
+        FROM subnet_groups
+        ORDER BY avg_score ASC, total_detections DESC
+        LIMIT %(limit)s
+        """
+
+        result = db.query(cluster_query, {"hours": hours, "limit": limit})
+        
+        # Collect sample IPs to fetch real UA and trend data in bulk
+        sample_ips = [row[10] for row in result.result_rows if row[10]]
+
+        # Fetch real primary UA per sample IP from view_dashboard_entities
+        ua_by_ip: dict = {}
+        if sample_ips:
+            ip_list_sql = ", ".join(f"'{ip}'" for ip in sample_ips[:50])
+            ua_query = f"""
+            SELECT entity_value, arrayElement(user_agents, 1) AS top_ua
+            FROM view_dashboard_entities
+            WHERE entity_type = 'ip'
+              AND entity_value IN ({ip_list_sql})
+              AND notEmpty(user_agents)
+            GROUP BY entity_value, top_ua
+            ORDER BY entity_value
+            """
+            try:
+                ua_result = db.query(ua_query)
+                for ua_row in ua_result.result_rows:
+                    if ua_row[0] not in ua_by_ip and ua_row[1]:
+                        ua_by_ip[str(ua_row[0])] = str(ua_row[1])
+            except Exception:
+                pass  # UA enrichment is best-effort
+
+        # Compute real trend: compare current window vs previous window of same duration
+        trend_query = """
+        WITH cleaned AS (
+            SELECT
+                replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS clean_ip,
+                detected_at,
+                concat(
+                    splitByChar('.', clean_ip)[1], '.',
+                    splitByChar('.', clean_ip)[2], '.',
+                    splitByChar('.', clean_ip)[3], '.0/24'
+                ) AS subnet
+            FROM ml_detected_anomalies
+        ),
+        current_window AS (
+            SELECT subnet, count() AS cnt
+            FROM cleaned
+            WHERE detected_at >= now() - INTERVAL %(hours)s HOUR
+            GROUP BY subnet
+        ),
+        prev_window AS (
+            SELECT subnet, count() AS cnt
+            FROM cleaned
+            WHERE detected_at >= now() - INTERVAL %(hours2)s HOUR
+              AND detected_at < now() - INTERVAL %(hours)s HOUR
+            GROUP BY subnet
+        )
+        SELECT c.subnet, c.cnt AS current_cnt, p.cnt AS prev_cnt
+        FROM current_window c
+        LEFT JOIN prev_window p ON c.subnet = p.subnet
+        """
+        trend_by_subnet: dict = {}
+        try:
+            trend_result = db.query(trend_query, {"hours": hours, "hours2": hours * 2})
+            for tr in trend_result.result_rows:
+                subnet_key = tr[0]
+                curr = tr[1] or 0
+                prev = tr[2] or 0
+                if prev == 0:
+                    trend_by_subnet[subnet_key] = ("new", 100)
+                else:
+                    pct = round(((curr - prev) / prev) * 100)
+                    trend_by_subnet[subnet_key] = ("up" if pct >= 0 else "down", abs(pct))
+        except Exception:
+            pass
+
+        clusters = []
+        for row in result.result_rows:
+            subnet = row[0]
+            threat_level = row[8] or 'LOW'
+            unique_ips = row[2] or 1
+            avg_score = abs(row[9] or 0)
+            sample_ip = row[10] if row[10] else subnet.split('/')[0]
+
+            critical_count = 1 if threat_level == 'CRITICAL' else 0
+            high_count = 1 if threat_level == 'HIGH' else 0
+
+            risk_score = min(100, round(
+                (critical_count * 30) +
+                (high_count * 20) +
+                (unique_ips * 5) +
+                (avg_score * 100)
+            ))
+
+            if critical_count > 0 or risk_score >= 80:
+                severity = "CRITICAL"
+            elif high_count > (row[1] or 1) * 0.3 or risk_score >= 60:
+                severity = "HIGH"
+            elif high_count > 0 or risk_score >= 40:
+                severity = "MEDIUM"
+            else:
+                severity = "LOW"
+
+            trend_dir, trend_pct = trend_by_subnet.get(subnet, ("stable", 0))
+            primary_ua = ua_by_ip.get(sample_ip, "")
+
+            clusters.append({
+                "id": f"INC-{hashlib.md5(subnet.encode()).hexdigest()[:8].upper()}",
+                "score": risk_score,
+                "severity": severity,
+                "total_detections": row[1],
+                "unique_ips": row[2],
+                "subnet": subnet,
+                "sample_ip": sample_ip,
+                "ja4": row[5] or "",
+                "primary_ua": primary_ua,
+                "primary_target": row[3].strftime('%H:%M') if row[3] else "Unknown",
+                "countries": [{"code": row[6] or "XX", "percentage": 100}],
+                "asn": str(row[7]) if row[7] else "",
+                "first_seen": row[3].isoformat() if row[3] else "",
+                "last_seen": row[4].isoformat() if row[4] else "",
+                "trend": trend_dir,
+                "trend_percentage": trend_pct,
+            })
+        
+        return {
+            "items": clusters,
+            "total": len(clusters),
+            "period_hours": hours
+        }
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+@router.get("/{cluster_id}")
+async def get_incident_details(cluster_id: str):
+    """
+    Récupère les détails d'un incident spécifique.
+    Non encore implémenté — les détails par cluster seront disponibles dans une prochaine version.
+    """
+    raise HTTPException(
+        status_code=501,
+        detail="Détails par incident non encore implémentés. Utilisez /api/incidents/clusters pour la liste."
+    )
+
+
+@router.post("/{cluster_id}/classify")
+async def classify_incident(
+    cluster_id: str,
+    label: str,
+    tags: List[str] = None,
+    comment: str = ""
+):
+    """
+    Classe un incident rapidement.
+    Non encore implémenté — utilisez /api/analysis/{ip}/classify pour classifier une IP.
+    """
+    raise HTTPException(
+        status_code=501,
+        detail="Classification par incident non encore implémentée. Utilisez /api/analysis/{ip}/classify."
+    )
+
+
+@router.get("")
+async def list_incidents(
+    status: str = Query("active", description="Statut des incidents"),
+    severity: Optional[str] = Query(None, description="Filtrer par sévérité (LOW/MEDIUM/HIGH/CRITICAL)"),
+    hours: int = Query(24, ge=1, le=168)
+):
+    """
+    Liste tous les incidents avec filtres.
+    Délègue à get_incident_clusters ; le filtre severity est appliqué post-requête.
+    """
+    try:
+        result = await get_incident_clusters(hours=hours, limit=100)
+        items = result["items"]
+
+        if severity:
+            sev_upper = severity.upper()
+            items = [c for c in items if c.get("severity") == sev_upper]
+
+        return {
+            "items": items,
+            "total": len(items),
+            "period_hours": hours,
+        }
+
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")

services/dashboard/backend/routes/investigation_summary.py

@@ -0,0 +1,185 @@
+"""
+Endpoint d'investigation enrichie pour une IP donnée.
+Agrège en une seule requête les données provenant de toutes les sources :
+ml_detected_anomalies, view_form_bruteforce_detected, view_tcp_spoofing_detected,
+agg_host_ip_ja4_1h (rotation JA4), view_ip_recurrence, view_ai_features_1h.
+"""
+from fastapi import APIRouter, HTTPException
+
+from ..database import db
+from ..services.tcp_fingerprint import fingerprint_os, detect_spoof, declared_os_from_ua
+
+router = APIRouter(prefix="/api/investigation", tags=["investigation"])
+
+
+@router.get(
+    "/{ip}/summary",
+    summary="Synthèse complète d'une IP",
+    response_description="Score de risque 0-100, détections ML, brute-force, spoofing TCP, rotation JA4, persistance et timeline 24h",
+)
+async def get_ip_full_summary(ip: str):
+    """
+    Synthèse complète pour une IP : toutes les sources en un appel.
+    Normalise l'IP (accepte ::ffff:x.x.x.x ou x.x.x.x).
+    """
+    clean_ip = ip.replace("::ffff:", "").strip()
+    try:
+        # ── 1. Score ML / features ─────────────────────────────────────────────
+        ml_sql = """
+        SELECT
+            max(abs(anomaly_score))        AS max_score,
+            any(threat_level)              AS threat_level,
+            any(bot_name)                  AS bot_name,
+            count()                        AS total_detections,
+            uniq(host)                     AS distinct_hosts,
+            uniq(ja4)                      AS distinct_ja4
+        FROM mabase_prod.ml_detected_anomalies
+        WHERE replaceRegexpAll(toString(src_ip), '^::ffff:', '') = %(ip)s
+        """
+        ml_res = db.query(ml_sql, {"ip": clean_ip})
+        ml_row = ml_res.result_rows[0] if ml_res.result_rows else None
+        ml_data = {
+            "max_score":        round(float(ml_row[0] or 0), 2) if ml_row else 0,
+            "threat_level":     str(ml_row[1] or "") if ml_row else "",
+            "attack_type":      str(ml_row[2] or "") if ml_row else "",
+            "total_detections": int(ml_row[3] or 0) if ml_row else 0,
+            "distinct_hosts":   int(ml_row[4] or 0) if ml_row else 0,
+            "distinct_ja4":     int(ml_row[5] or 0) if ml_row else 0,
+        }
+
+        # ── 2. Brute force ─────────────────────────────────────────────────────
+        bf_sql = """
+        SELECT
+            uniq(host)              AS hosts_attacked,
+            sum(hits)               AS total_hits,
+            sum(query_params_count) AS total_params,
+            groupArray(3)(host)     AS top_hosts
+        FROM mabase_prod.view_form_bruteforce_detected
+        WHERE replaceRegexpAll(toString(src_ip), '^::ffff:', '') = %(ip)s
+        """
+        bf_res = db.query(bf_sql, {"ip": clean_ip})
+        bf_row = bf_res.result_rows[0] if bf_res.result_rows else None
+        bf_data = {
+            "active": bool(bf_row and int(bf_row[1] or 0) > 0),
+            "hosts_attacked": int(bf_row[0] or 0) if bf_row else 0,
+            "total_hits":     int(bf_row[1] or 0) if bf_row else 0,
+            "total_params":   int(bf_row[2] or 0) if bf_row else 0,
+            "top_hosts":      [str(h) for h in (bf_row[3] or [])] if bf_row else [],
+        }
+
+        # ── 3. TCP spoofing — fingerprinting multi-signal ─────────────────────
+        tcp_sql = """
+        SELECT
+            any(tcp_ttl_raw)   AS ttl,
+            any(tcp_win_raw)   AS win,
+            any(tcp_scale_raw) AS scale,
+            any(tcp_mss_raw)   AS mss,
+            any(first_ua)      AS ua
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE replaceRegexpAll(toString(src_ip), '^::ffff:', '') = %(ip)s
+          AND window_start >= now() - INTERVAL 24 HOUR
+          AND tcp_ttl_raw > 0
+        LIMIT 1
+        """
+        tcp_res = db.query(tcp_sql, {"ip": clean_ip})
+        tcp_data = {"detected": False, "tcp_ttl": None, "suspected_os": None}
+        if tcp_res.result_rows:
+            r     = tcp_res.result_rows[0]
+            ttl   = int(r[0] or 0)
+            win   = int(r[1] or 0)
+            scale = int(r[2] or 0)
+            mss   = int(r[3] or 0)
+            ua    = str(r[4] or "")
+            fp         = fingerprint_os(ttl, win, scale, mss)
+            dec_os     = declared_os_from_ua(ua)
+            spoof_res  = detect_spoof(fp, dec_os)
+            tcp_data = {
+                "detected":      spoof_res.is_spoof,
+                "tcp_ttl":       ttl,
+                "tcp_mss":       mss,
+                "tcp_win_scale": scale,
+                "initial_ttl":   fp.initial_ttl,
+                "hop_count":     fp.hop_count,
+                "suspected_os":  fp.os_name,
+                "declared_os":   dec_os,
+                "confidence":    fp.confidence,
+                "network_path":  fp.network_path,
+                "is_bot_tool":   fp.is_bot_tool,
+                "spoof_reason":  spoof_res.reason,
+            }
+
+        # ── 4. JA4 rotation ────────────────────────────────────────────────────
+        rot_sql = """
+        SELECT distinct_ja4_count, total_hits
+        FROM mabase_prod.view_host_ip_ja4_rotation
+        WHERE replaceRegexpAll(toString(src_ip), '^::ffff:', '') = %(ip)s
+        LIMIT 1
+        """
+        rot_res = db.query(rot_sql, {"ip": clean_ip})
+        rot_data = {"rotating": False, "distinct_ja4_count": 0}
+        if rot_res.result_rows:
+            row = rot_res.result_rows[0]
+            cnt = int(row[0] or 0)
+            rot_data = {"rotating": cnt > 1, "distinct_ja4_count": cnt, "total_hits": int(row[1] or 0)}
+
+        # ── 5. Persistance ─────────────────────────────────────────────────────
+        pers_sql = """
+        SELECT recurrence, worst_score, worst_threat_level, first_seen, last_seen
+        FROM mabase_prod.view_ip_recurrence
+        WHERE replaceRegexpAll(toString(src_ip), '^::ffff:', '') = %(ip)s
+        LIMIT 1
+        """
+        pers_res = db.query(pers_sql, {"ip": clean_ip})
+        pers_data = {"persistent": False, "recurrence": 0}
+        if pers_res.result_rows:
+            row = pers_res.result_rows[0]
+            pers_data = {
+                "persistent":        True,
+                "recurrence":        int(row[0] or 0),
+                "worst_score":       round(float(row[1] or 0), 2),
+                "worst_threat_level":str(row[2] or ""),
+                "first_seen":        str(row[3]),
+                "last_seen":         str(row[4]),
+            }
+
+        # ── 6. Timeline 24h ────────────────────────────────────────────────────
+        tl_sql = """
+        SELECT
+            toHour(window_start)  AS hour,
+            sum(hits)             AS hits,
+            groupUniqArray(3)(ja4) AS ja4s
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE replaceRegexpAll(toString(src_ip), '^::ffff:', '') = %(ip)s
+          AND window_start >= now() - INTERVAL 24 HOUR
+        GROUP BY hour
+        ORDER BY hour ASC
+        """
+        tl_res = db.query(tl_sql, {"ip": clean_ip})
+        timeline = [
+            {"hour": int(r[0]), "hits": int(r[1]), "ja4s": [str(j) for j in (r[2] or [])]}
+            for r in tl_res.result_rows
+        ]
+
+        # ── Global risk score (heuristic) ──────────────────────────────────────
+        risk = 0
+        risk += min(50, ml_data["max_score"] * 50)
+        if bf_data["active"]:        risk += 20
+        if tcp_data["detected"]:
+            if tcp_data.get("is_bot_tool"):   risk += 30  # outil de scan connu
+            else:                             risk += 15  # spoof OS
+        if rot_data["rotating"]:     risk += min(15, rot_data["distinct_ja4_count"] * 3)
+        if pers_data["persistent"]:  risk += min(10, pers_data["recurrence"] * 2)
+        risk = min(100, round(risk))
+
+        return {
+            "ip":          clean_ip,
+            "risk_score":  risk,
+            "ml":          ml_data,
+            "bruteforce":  bf_data,
+            "tcp_spoofing":tcp_data,
+            "ja4_rotation":rot_data,
+            "persistence": pers_data,
+            "timeline_24h":timeline,
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

services/dashboard/backend/routes/metrics.py

@@ -0,0 +1,175 @@
+"""
+Endpoints pour les métriques du dashboard
+"""
+from fastapi import APIRouter, HTTPException
+from ..database import db
+from ..models import MetricsResponse, MetricsSummary, TimeSeriesPoint
+
+router = APIRouter(prefix="/api/metrics", tags=["metrics"])
+
+
+@router.get("", response_model=MetricsResponse, summary="Métriques globales du dashboard")
+async def get_metrics():
+    """
+    Récupère les métriques globales du dashboard
+    """
+    try:
+        # Résumé des métriques
+        summary_query = """
+        SELECT
+            count() AS total_detections,
+            countIf(threat_level = 'CRITICAL') AS critical_count,
+            countIf(threat_level = 'HIGH') AS high_count,
+            countIf(threat_level = 'MEDIUM') AS medium_count,
+            countIf(threat_level = 'LOW') AS low_count,
+            countIf(bot_name != '') AS known_bots_count,
+            countIf(bot_name = '') AS anomalies_count,
+            uniq(src_ip) AS unique_ips
+        FROM ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL 24 HOUR
+        """
+        
+        summary_result = db.query(summary_query)
+        summary_row = summary_result.result_rows[0] if summary_result.result_rows else None
+        
+        if not summary_row:
+            raise HTTPException(status_code=404, detail="Aucune donnée disponible")
+        
+        summary = MetricsSummary(
+            total_detections=summary_row[0],
+            critical_count=summary_row[1],
+            high_count=summary_row[2],
+            medium_count=summary_row[3],
+            low_count=summary_row[4],
+            known_bots_count=summary_row[5],
+            anomalies_count=summary_row[6],
+            unique_ips=summary_row[7]
+        )
+        
+        # Série temporelle (par heure)
+        timeseries_query = """
+        SELECT
+            toStartOfHour(detected_at) AS hour,
+            count() AS total,
+            countIf(threat_level = 'CRITICAL') AS critical,
+            countIf(threat_level = 'HIGH') AS high,
+            countIf(threat_level = 'MEDIUM') AS medium,
+            countIf(threat_level = 'LOW') AS low
+        FROM ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL 24 HOUR
+        GROUP BY hour
+        ORDER BY hour
+        """
+        
+        timeseries_result = db.query(timeseries_query)
+        timeseries = [
+            TimeSeriesPoint(
+                hour=row[0],
+                total=row[1],
+                critical=row[2],
+                high=row[3],
+                medium=row[4],
+                low=row[5]
+            )
+            for row in timeseries_result.result_rows
+        ]
+        
+        # Distribution par menace
+        threat_distribution = {
+            "CRITICAL": summary.critical_count,
+            "HIGH": summary.high_count,
+            "MEDIUM": summary.medium_count,
+            "LOW": summary.low_count
+        }
+        
+        return MetricsResponse(
+            summary=summary,
+            timeseries=timeseries,
+            threat_distribution=threat_distribution
+        )
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur lors de la récupération des métriques: {str(e)}")
+
+
+@router.get("/threats")
+async def get_threat_distribution():
+    """
+    Récupère la répartition par niveau de menace
+    """
+    try:
+        query = """
+        SELECT
+            threat_level,
+            count() AS count,
+            round(count() * 100.0 / sum(count()) OVER (), 2) AS percentage
+        FROM ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL 24 HOUR
+        GROUP BY threat_level
+        ORDER BY count DESC
+        """
+        
+        result = db.query(query)
+        
+        return {
+            "items": [
+                {"threat_level": row[0], "count": row[1], "percentage": row[2]}
+                for row in result.result_rows
+            ]
+        }
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+@router.get("/baseline")
+async def get_metrics_baseline():
+    """
+    Compare les métriques actuelles (24h) vs hier (24h-48h) pour afficher les tendances.
+    """
+    try:
+        query = """
+        SELECT
+            countIf(detected_at >= now() - INTERVAL 24 HOUR)                            AS today_total,
+            countIf(detected_at >= now() - INTERVAL 48 HOUR AND detected_at < now() - INTERVAL 24 HOUR) AS yesterday_total,
+            uniqIf(src_ip, detected_at >= now() - INTERVAL 24 HOUR)                     AS today_ips,
+            uniqIf(src_ip, detected_at >= now() - INTERVAL 48 HOUR AND detected_at < now() - INTERVAL 24 HOUR) AS yesterday_ips,
+            countIf(threat_level = 'CRITICAL' AND detected_at >= now() - INTERVAL 24 HOUR) AS today_critical,
+            countIf(threat_level = 'CRITICAL' AND detected_at >= now() - INTERVAL 48 HOUR AND detected_at < now() - INTERVAL 24 HOUR) AS yesterday_critical
+        FROM ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL 48 HOUR
+        """
+        r = db.query(query)
+        row = r.result_rows[0] if r.result_rows else None
+
+        def pct_change(today: int, yesterday: int) -> float:
+            if yesterday == 0:
+                return 100.0 if today > 0 else 0.0
+            return round((today - yesterday) / yesterday * 100, 1)
+
+        today_total     = int(row[0] or 0) if row else 0
+        yesterday_total = int(row[1] or 0) if row else 0
+        today_ips       = int(row[2] or 0) if row else 0
+        yesterday_ips   = int(row[3] or 0) if row else 0
+        today_crit      = int(row[4] or 0) if row else 0
+        yesterday_crit  = int(row[5] or 0) if row else 0
+
+        return {
+            "total_detections": {
+                "today":      today_total,
+                "yesterday":  yesterday_total,
+                "pct_change": pct_change(today_total, yesterday_total),
+            },
+            "unique_ips": {
+                "today":      today_ips,
+                "yesterday":  yesterday_ips,
+                "pct_change": pct_change(today_ips, yesterday_ips),
+            },
+            "critical_alerts": {
+                "today":      today_crit,
+                "yesterday":  yesterday_crit,
+                "pct_change": pct_change(today_crit, yesterday_crit),
+            },
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur baseline: {str(e)}")

services/dashboard/backend/routes/ml_features.py

@@ -0,0 +1,425 @@
+"""
+Endpoints pour les features ML / IA (scores d'anomalies, radar, scatter)
+"""
+from fastapi import APIRouter, HTTPException, Query
+
+from ..database import db
+
+router = APIRouter(prefix="/api/ml", tags=["ml_features"])
+
+
+def _attack_type(fuzzing_index: float, hit_velocity: float,
+                 is_fake_nav: int, ua_ch_mismatch: int) -> str:
+    if fuzzing_index > 50:
+        return "brute_force"
+    if hit_velocity > 1.0:
+        return "flood"
+    if is_fake_nav:
+        return "scraper"
+    if ua_ch_mismatch:
+        return "spoofing"
+    return "scanner"
+
+
+@router.get("/top-anomalies")
+async def get_top_anomalies(limit: int = Query(50, ge=1, le=500)):
+    """Top IPs anomales (24h) — bypass view_ai_features_1h pour éviter les window functions.
+    Query directe sur agg_host_ip_ja4_1h + LEFT JOIN agg_header_fingerprint_1h.
+    """
+    try:
+        sql = """
+        SELECT
+            replaceRegexpAll(toString(a.src_ip), '^::ffff:', '')                                AS ip,
+            any(a.ja4)                                                                           AS ja4,
+            any(a.host)                                                                          AS host,
+            sum(a.hits)                                                                          AS hits,
+            round(uniqMerge(a.uniq_query_params)
+                  / greatest(uniqMerge(a.uniq_paths), 1), 4)                                    AS fuzzing_index,
+            round(sum(a.hits)
+                  / greatest(dateDiff('second', min(a.first_seen), max(a.last_seen)), 1), 2)    AS hit_velocity,
+            round(sum(a.count_head)         / greatest(sum(a.hits), 1), 4)                      AS head_ratio,
+            round(sum(a.count_no_sec_fetch) / greatest(sum(a.hits), 1), 4)                      AS sec_fetch_absence,
+            round(sum(a.tls12_count)        / greatest(sum(a.hits), 1), 4)                      AS tls12_ratio,
+            round(sum(a.count_generic_accept) / greatest(sum(a.hits), 1), 4)                    AS generic_accept_ratio,
+            any(a.src_country_code)                                                              AS country,
+            any(a.src_as_name)                                                                   AS asn_name,
+            max(h.ua_ch_mismatch)                                                                AS ua_ch_mismatch,
+            max(h.modern_browser_score)                                                          AS browser_score,
+            dictGetOrDefault('mabase_prod.dict_asn_reputation', 'label', toUInt64(any(a.src_asn)), 'unknown') AS asn_label,
+            coalesce(
+                nullIf(dictGetOrDefault('mabase_prod.dict_bot_ja4', 'bot_name', tuple(any(a.ja4)), ''), ''),
+                ''
+            ) AS bot_name
+        FROM mabase_prod.agg_host_ip_ja4_1h a
+        LEFT JOIN mabase_prod.agg_header_fingerprint_1h h
+              ON a.src_ip = h.src_ip AND a.window_start = h.window_start
+        WHERE a.window_start >= now() - INTERVAL 24 HOUR
+        GROUP BY a.src_ip
+        ORDER BY fuzzing_index DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"limit": limit})
+        items = []
+        for row in result.result_rows:
+            fuzzing  = float(row[4] or 0)
+            velocity = float(row[5] or 0)
+            ua_mm    = int(row[12] or 0)
+            items.append({
+                "ip":                    str(row[0]),
+                "ja4":                   str(row[1]),
+                "host":                  str(row[2]),
+                "hits":                  int(row[3] or 0),
+                "fuzzing_index":         fuzzing,
+                "hit_velocity":          velocity,
+                "head_ratio":            float(row[6] or 0),
+                "sec_fetch_absence":     float(row[7] or 0),
+                "tls12_ratio":           float(row[8] or 0),
+                "generic_accept_ratio":  float(row[9] or 0),
+                "country":               str(row[10] or ""),
+                "asn_name":              str(row[11] or ""),
+                "ua_ch_mismatch":        ua_mm,
+                "browser_score":         int(row[13] or 0),
+                "asn_label":             str(row[14] or ""),
+                "bot_name":              str(row[15] or ""),
+                "attack_type":           _attack_type(fuzzing, velocity, 0, ua_mm),
+            })
+        return {"items": items}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/ip/{ip}/radar")
+async def get_ip_radar(ip: str):
+    """Scores radar pour une IP spécifique (8 dimensions d'anomalie)."""
+    try:
+        sql = """
+        SELECT
+            avg(fuzzing_index)              AS fuzzing_index,
+            avg(hit_velocity)               AS hit_velocity,
+            avg(is_fake_navigation)         AS is_fake_navigation,
+            avg(ua_ch_mismatch)             AS ua_ch_mismatch,
+            avg(sni_host_mismatch)          AS sni_host_mismatch,
+            avg(orphan_ratio)               AS orphan_ratio,
+            avg(path_diversity_ratio)       AS path_diversity_ratio,
+            avg(anomalous_payload_ratio)    AS anomalous_payload_ratio
+        FROM mabase_prod.view_ai_features_1h
+        WHERE replaceRegexpAll(toString(src_ip), '^::ffff:', '') = %(ip)s
+          AND window_start >= now() - INTERVAL 24 HOUR
+        """
+        result = db.query(sql, {"ip": ip})
+        if not result.result_rows:
+            raise HTTPException(status_code=404, detail="IP not found")
+        row = result.result_rows[0]
+
+        def _f(v) -> float:
+            return float(v or 0)
+
+        return {
+            "ip":                    ip,
+            "fuzzing_score":         min(100.0, _f(row[0])),
+            "velocity_score":        min(100.0, _f(row[1]) * 100),
+            "fake_nav_score":        _f(row[2]) * 100,
+            "ua_mismatch_score":     _f(row[3]) * 100,
+            "sni_mismatch_score":    _f(row[4]) * 100,
+            "orphan_score":          min(100.0, _f(row[5]) * 100),
+            "path_repetition_score": max(0.0, 100 - _f(row[6]) * 100),
+            "payload_anomaly_score": min(100.0, _f(row[7]) * 100),
+        }
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/score-distribution")
+async def get_score_distribution():
+    """
+    Distribution de TOUS les scores ML depuis ml_all_scores (3j).
+    Single query avec conditional aggregates pour éviter le double scan.
+    """
+    try:
+        # Single scan — global totals + per-model breakdown via GROUPING SETS
+        sql = """
+        SELECT
+            threat_level,
+            model_name,
+            count()                      AS total,
+            round(avg(anomaly_score), 4) AS avg_score,
+            round(min(anomaly_score), 4) AS min_score,
+            countIf(threat_level = 'NORMAL')   AS normal_count,
+            countIf(threat_level NOT IN ('NORMAL','KNOWN_BOT')) AS anomaly_count,
+            countIf(threat_level = 'KNOWN_BOT') AS bot_count
+        FROM mabase_prod.ml_all_scores
+        WHERE detected_at >= now() - INTERVAL 3 DAY
+        GROUP BY threat_level, model_name
+        ORDER BY model_name, total DESC
+        """
+        result = db.query(sql)
+        by_model: dict = {}
+        grand_total = 0
+        total_normal = total_anomaly = total_bot = 0
+        for row in result.result_rows:
+            level     = str(row[0])
+            model     = str(row[1])
+            total     = int(row[2])
+            grand_total += total
+            total_normal  += int(row[5] or 0)
+            total_anomaly += int(row[6] or 0)
+            total_bot     += int(row[7] or 0)
+            if model not in by_model:
+                by_model[model] = []
+            by_model[model].append({
+                "threat_level": level,
+                "total":        total,
+                "avg_score":    float(row[3] or 0),
+                "min_score":    float(row[4] or 0),
+            })
+
+        grand_total = max(grand_total, 1)
+        return {
+            "by_model": by_model,
+            "totals": {
+                "normal":      total_normal,
+                "anomaly":     total_anomaly,
+                "known_bot":   total_bot,
+                "grand_total": grand_total,
+                "normal_pct":  round(total_normal  / grand_total * 100, 1),
+                "anomaly_pct": round(total_anomaly / grand_total * 100, 1),
+                "bot_pct":     round(total_bot     / grand_total * 100, 1),
+            }
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/score-trends")
+async def get_score_trends(hours: int = Query(72, ge=1, le=168)):
+    """
+    Évolution temporelle des scores ML depuis ml_all_scores.
+    Retourne le score moyen et les counts par heure et par modèle.
+    """
+    try:
+        sql = """
+        SELECT
+            toStartOfHour(window_start)      AS hour,
+            model_name,
+            countIf(threat_level = 'NORMAL') AS normal_count,
+            countIf(threat_level IN ('LOW','MEDIUM','HIGH','CRITICAL')) AS anomaly_count,
+            countIf(threat_level = 'KNOWN_BOT') AS bot_count,
+            round(avgIf(anomaly_score, threat_level IN ('LOW','MEDIUM','HIGH','CRITICAL')), 4) AS avg_anomaly_score
+        FROM mabase_prod.ml_all_scores
+        WHERE window_start >= now() - INTERVAL %(hours)s HOUR
+        GROUP BY hour, model_name
+        ORDER BY hour ASC, model_name
+        """
+        result = db.query(sql, {"hours": hours})
+        points = []
+        for row in result.result_rows:
+            points.append({
+                "hour":           str(row[0]),
+                "model":          str(row[1]),
+                "normal_count":   int(row[2] or 0),
+                "anomaly_count":  int(row[3] or 0),
+                "bot_count":      int(row[4] or 0),
+                "avg_anomaly_score": float(row[5] or 0),
+            })
+        return {"points": points, "hours": hours}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/b-features")
+async def get_b_features(limit: int = Query(50, ge=1, le=200)):
+    """
+    Agrégation des B-features (HTTP pures) pour les top IPs anomales.
+    Source: agg_host_ip_ja4_1h (SimpleAggregateFunction columns).
+    Expose: head_ratio, sec_fetch_absence, tls12_ratio, generic_accept_ratio, http10_ratio.
+    Ces features sont calculées dans view_ai_features_1h mais jamais visualisées dans le dashboard.
+    """
+    try:
+        sql = """
+        SELECT ip, ja4, country, asn_name, total_hits AS hits,
+               head_ratio, sec_fetch_absence, tls12_ratio, generic_accept_ratio, http10_ratio,
+               missing_accept_enc_ratio, http_scheme_ratio
+        FROM (
+            SELECT
+                replaceRegexpAll(toString(src_ip), '^::ffff:', '')          AS ip,
+                any(ja4)                                                     AS ja4,
+                any(src_country_code)                                        AS country,
+                any(src_as_name)                                             AS asn_name,
+                sum(hits)                                                    AS total_hits,
+                round(sum(count_head)             / greatest(sum(hits),1), 4) AS head_ratio,
+                round(sum(count_no_sec_fetch)     / greatest(sum(hits),1), 4) AS sec_fetch_absence,
+                round(sum(tls12_count)            / greatest(sum(hits),1), 4) AS tls12_ratio,
+                round(sum(count_generic_accept)   / greatest(sum(hits),1), 4) AS generic_accept_ratio,
+                round(sum(count_http10)           / greatest(sum(hits),1), 4) AS http10_ratio,
+                round(sum(count_no_accept_enc)    / greatest(sum(hits),1), 4) AS missing_accept_enc_ratio,
+                round(sum(count_http_scheme)      / greatest(sum(hits),1), 4) AS http_scheme_ratio
+            FROM mabase_prod.agg_host_ip_ja4_1h
+            WHERE window_start >= now() - INTERVAL 24 HOUR
+            GROUP BY src_ip
+        )
+        WHERE sec_fetch_absence > 0.5 OR generic_accept_ratio > 0.3
+           OR head_ratio > 0.1 OR tls12_ratio > 0.5 OR missing_accept_enc_ratio > 0.3
+        ORDER BY (head_ratio + sec_fetch_absence + generic_accept_ratio + missing_accept_enc_ratio) DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"limit": limit})
+        items = []
+        for row in result.result_rows:
+            items.append({
+                "ip":                      str(row[0]),
+                "ja4":                     str(row[1] or ""),
+                "country":                 str(row[2] or ""),
+                "asn_name":                str(row[3] or ""),
+                "hits":                    int(row[4] or 0),
+                "head_ratio":              float(row[5] or 0),
+                "sec_fetch_absence":       float(row[6] or 0),
+                "tls12_ratio":             float(row[7] or 0),
+                "generic_accept_ratio":    float(row[8] or 0),
+                "http10_ratio":            float(row[9] or 0),
+                "missing_accept_enc_ratio":float(row[10] or 0),
+                "http_scheme_ratio":       float(row[11] or 0),
+            })
+        return {"items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/campaigns")
+async def get_ml_campaigns(hours: int = Query(24, ge=1, le=168), limit: int = Query(20, ge=1, le=100)):
+    """
+    Groupes d'anomalies détectées par DBSCAN (campaign_id >= 0).
+    Si aucune campagne active, fallback sur clustering par /24 subnet + JA4 commun.
+    Utile pour détecter les botnets distribués sans état de campagne DBSCAN.
+    """
+    try:
+        # First: check real campaigns
+        campaign_sql = """
+        SELECT
+            campaign_id,
+            count()                                                     AS total_detections,
+            uniq(src_ip)                                                AS unique_ips,
+            any(threat_level)                                           AS dominant_threat,
+            groupUniqArray(3)(threat_level)                             AS threat_levels,
+            groupUniqArray(3)(bot_name)                                 AS bot_names,
+            min(detected_at)                                            AS first_seen,
+            max(detected_at)                                            AS last_seen
+        FROM mabase_prod.ml_detected_anomalies
+        WHERE detected_at >= now() - INTERVAL %(hours)s HOUR
+          AND campaign_id >= 0
+        GROUP BY campaign_id
+        ORDER BY total_detections DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(campaign_sql, {"hours": hours, "limit": limit})
+        campaigns = []
+        for row in result.result_rows:
+            campaigns.append({
+                "id":               f"C{row[0]}",
+                "campaign_id":      int(row[0]),
+                "total_detections": int(row[1]),
+                "unique_ips":       int(row[2]),
+                "dominant_threat":  str(row[3] or ""),
+                "threat_levels":    list(row[4] or []),
+                "bot_names":        list(row[5] or []),
+                "first_seen":       str(row[6]),
+                "last_seen":        str(row[7]),
+                "source":           "dbscan",
+            })
+
+        # Fallback: subnet-based clustering when DBSCAN has no campaigns
+        if not campaigns:
+            subnet_sql = """
+            SELECT
+                IPv4CIDRToRange(toIPv4(replaceRegexpAll(toString(src_ip),'^::ffff:','')), 24).1 AS subnet,
+                count()                             AS total_detections,
+                uniq(src_ip)                        AS unique_ips,
+                groupArray(3)(threat_level)         AS threat_levels,
+                any(bot_name)                       AS bot_name,
+                any(ja4)                            AS sample_ja4,
+                min(detected_at)                    AS first_seen,
+                max(detected_at)                    AS last_seen
+            FROM mabase_prod.ml_detected_anomalies
+            WHERE detected_at >= now() - INTERVAL %(hours)s HOUR
+              AND threat_level IN ('HIGH','CRITICAL','MEDIUM')
+            GROUP BY subnet
+            HAVING unique_ips >= 3
+            ORDER BY total_detections DESC
+            LIMIT %(limit)s
+            """
+            result2 = db.query(subnet_sql, {"hours": hours, "limit": limit})
+            for i, row in enumerate(result2.result_rows):
+                subnet_str = str(row[0]) + "/24"
+                campaigns.append({
+                    "id":               f"S{i+1:03d}",
+                    "campaign_id":      -1,
+                    "subnet":           subnet_str,
+                    "total_detections": int(row[1]),
+                    "unique_ips":       int(row[2]),
+                    "dominant_threat":  str((row[3] or [""])[0]),
+                    "threat_levels":    list(row[3] or []),
+                    "bot_names":        [str(row[4] or "")],
+                    "sample_ja4":       str(row[5] or ""),
+                    "first_seen":       str(row[6]),
+                    "last_seen":        str(row[7]),
+                    "source":           "subnet_cluster",
+                })
+
+        dbscan_active = any(c["campaign_id"] >= 0 for c in campaigns)
+        return {
+            "campaigns":      campaigns,
+            "total":          len(campaigns),
+            "dbscan_active":  dbscan_active,
+            "hours":          hours,
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/scatter")
+async def get_ml_scatter(limit: int = Query(200, ge=1, le=1000)):
+    """Points scatter plot (fuzzing_index × hit_velocity) — bypass view_ai_features_1h."""
+    try:
+        sql = """
+        SELECT
+            ip,
+            ja4,
+            round(fuzzing_index, 4)                                                              AS fuzzing_index,
+            round(total_hits / greatest(dateDiff('second', min_first, max_last), 1), 2)         AS hit_velocity,
+            total_hits                                                                            AS hits,
+            round(total_count_head / greatest(total_hits, 1), 4)                                AS head_ratio,
+            correlated
+        FROM (
+            SELECT
+                replaceRegexpAll(toString(src_ip), '^::ffff:', '')                               AS ip,
+                any(ja4)                                                                          AS ja4,
+                uniqMerge(uniq_query_params) / greatest(uniqMerge(uniq_paths), 1)               AS fuzzing_index,
+                sum(hits)                                                                         AS total_hits,
+                min(first_seen)                                                                   AS min_first,
+                max(last_seen)                                                                    AS max_last,
+                sum(count_head)                                                                   AS total_count_head,
+                max(correlated_raw)                                                               AS correlated
+            FROM mabase_prod.agg_host_ip_ja4_1h
+            WHERE window_start >= now() - INTERVAL 24 HOUR
+            GROUP BY src_ip
+        )
+        ORDER BY fuzzing_index DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"limit": limit})
+        points = []
+        for row in result.result_rows:
+            fuzzing  = float(row[2] or 0)
+            velocity = float(row[3] or 0)
+            points.append({
+                "ip":           str(row[0]),
+                "ja4":          str(row[1]),
+                "fuzzing_index":fuzzing,
+                "hit_velocity": velocity,
+                "hits":         int(row[4] or 0),
+                "attack_type":  _attack_type(fuzzing, velocity, 0, 0),
+            })
+        return {"points": points}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

services/dashboard/backend/routes/reputation.py

@@ -0,0 +1,125 @@
+"""
+Routes pour la réputation IP (bases de données publiques)
+"""
+from fastapi import APIRouter, HTTPException, Path
+from typing import Dict, Any
+import re
+
+from ..services.reputation_ip import get_reputation_service
+
+router = APIRouter(prefix="/api/reputation", tags=["Reputation"])
+
+# Pattern de validation d'IP (IPv4)
+IP_PATTERN = re.compile(r'^(\d{1,3}\.){3}\d{1,3}$')
+
+
+def is_valid_ipv4(ip: str) -> bool:
+    """Valide qu'une chaîne est une adresse IPv4 valide"""
+    if not IP_PATTERN.match(ip):
+        return False
+    
+    # Vérifie que chaque octet est entre 0 et 255
+    parts = ip.split('.')
+    for part in parts:
+        try:
+            num = int(part)
+            if num < 0 or num > 255:
+                return False
+        except ValueError:
+            return False
+    
+    return True
+
+
+@router.get("/ip/{ip_address}", summary="Réputation complète d'une IP")
+async def get_ip_reputation(
+    ip_address: str = Path(..., description="Adresse IP à vérifier")
+) -> Dict[str, Any]:
+    """
+    Récupère la réputation d'une adresse IP depuis les bases de données publiques
+    
+    Sources utilisées (sans clé API):
+    - IP-API.com: Géolocalisation + Proxy/Hosting detection
+    - IPinfo.io: ASN + Organisation
+    
+    Returns:
+        Dict avec:
+        - ip: Adresse IP vérifiée
+        - timestamp: Date de la vérification
+        - sources: Détails par source
+        - aggregated: Résultats agrégés
+            - is_proxy: bool
+            - is_hosting: bool
+            - is_vpn: bool
+            - is_tor: bool
+            - threat_score: 0-100
+            - threat_level: clean/low/medium/high/critical
+            - country: Pays
+            - asn: Numéro ASN
+            - asn_org: Organisation ASN
+            - org: ISP/Organisation
+            - warnings: Liste des alertes
+    """
+    # Valide l'adresse IP
+    if not is_valid_ipv4(ip_address):
+        raise HTTPException(
+            status_code=400,
+            detail=f"Adresse IP invalide: {ip_address}. Format attendu: x.x.x.x"
+        )
+    
+    try:
+        # Récupère le service de réputation
+        reputation_service = get_reputation_service()
+        
+        # Interroge les sources
+        results = await reputation_service.get_reputation(ip_address)
+        
+        return results
+        
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Erreur lors de la vérification de réputation: {str(e)}"
+        )
+
+
+@router.get("/ip/{ip_address}/summary", summary="Réputation simplifiée d'une IP")
+async def get_ip_reputation_summary(
+    ip_address: str = Path(..., description="Adresse IP à vérifier")
+) -> Dict[str, Any]:
+    """
+    Version simplifiée de la réputation IP (juste les infos essentielles)
+    
+    Utile pour affichage rapide dans les tableaux
+    """
+    if not is_valid_ipv4(ip_address):
+        raise HTTPException(
+            status_code=400,
+            detail=f"Adresse IP invalide: {ip_address}"
+        )
+    
+    try:
+        reputation_service = get_reputation_service()
+        full_results = await reputation_service.get_reputation(ip_address)
+        
+        # Retourne juste l'essentiel
+        aggregated = full_results.get('aggregated', {})
+        
+        return {
+            'ip': ip_address,
+            'threat_level': aggregated.get('threat_level', 'unknown'),
+            'threat_score': aggregated.get('threat_score', 0),
+            'is_proxy': aggregated.get('is_proxy', False),
+            'is_hosting': aggregated.get('is_hosting', False),
+            'country': aggregated.get('country'),
+            'country_code': aggregated.get('country_code'),
+            'asn': aggregated.get('asn'),
+            'org': aggregated.get('org'),
+            'warnings_count': len(aggregated.get('warnings', []))
+        }
+        
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Erreur: {str(e)}"
+        )

services/dashboard/backend/routes/rotation.py

@@ -0,0 +1,217 @@
+"""
+Endpoints pour la détection de la rotation de fingerprints JA4 et des menaces persistantes
+"""
+from fastapi import APIRouter, HTTPException, Query
+
+from ..database import db
+
+router = APIRouter(prefix="/api/rotation", tags=["rotation"])
+
+
+@router.get("/ja4-rotators")
+async def get_ja4_rotators(limit: int = Query(50, ge=1, le=500)):
+    """IPs qui effectuent le plus de rotation de fingerprints JA4."""
+    try:
+        sql = """
+        SELECT
+            replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS ip,
+            distinct_ja4_count,
+            total_hits
+        FROM mabase_prod.view_host_ip_ja4_rotation
+        ORDER BY distinct_ja4_count DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"limit": limit})
+        items = []
+        for row in result.result_rows:
+            distinct = int(row[1])
+            items.append({
+                "ip":                str(row[0]),
+                "distinct_ja4_count":distinct,
+                "total_hits":        int(row[2]),
+                "evasion_score":     min(100, distinct * 15),
+            })
+        return {"items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/persistent-threats")
+async def get_persistent_threats(limit: int = Query(100, ge=1, le=1000)):
+    """Menaces persistantes triées par score de persistance."""
+    try:
+        sql = """
+        SELECT
+            replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS ip,
+            recurrence,
+            worst_score,
+            worst_threat_level,
+            first_seen,
+            last_seen
+        FROM mabase_prod.view_ip_recurrence
+        ORDER BY (least(100, recurrence * 20 + worst_score * 50)) DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"limit": limit})
+        items = []
+        for row in result.result_rows:
+            recurrence  = int(row[1])
+            worst_score = float(row[2] or 0)
+            items.append({
+                "ip":                str(row[0]),
+                "recurrence":        recurrence,
+                "worst_score":       worst_score,
+                "worst_threat_level":str(row[3] or ""),
+                "first_seen":        str(row[4]),
+                "last_seen":         str(row[5]),
+                "persistence_score": min(100, recurrence * 20 + worst_score * 50),
+            })
+        return {"items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/ip/{ip}/ja4-history")
+async def get_ip_ja4_history(ip: str):
+    """Historique des JA4 utilisés par une IP donnée."""
+    try:
+        sql = """
+        SELECT
+            ja4,
+            sum(hits)       AS hits,
+            min(window_start) AS first_seen,
+            max(window_start) AS last_seen
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE replaceRegexpAll(toString(src_ip), '^::ffff:', '') = %(ip)s
+        GROUP BY ja4
+        ORDER BY hits DESC
+        """
+        result = db.query(sql, {"ip": ip})
+        items = [
+            {
+                "ja4":       str(row[0]),
+                "hits":      int(row[1]),
+                "first_seen":str(row[2]),
+                "last_seen": str(row[3]),
+            }
+            for row in result.result_rows
+        ]
+        return {"ip": ip, "ja4_history": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/sophistication")
+async def get_sophistication(limit: int = Query(50, ge=1, le=500)):
+    """Score de sophistication adversaire par IP (rotation JA4 + récurrence + bruteforce).
+    Single SQL JOIN query — aucun traitement Python sur 34K entrées.
+    """
+    try:
+        sql = """
+        SELECT
+            r.ip,
+            r.distinct_ja4_count,
+            coalesce(rec.recurrence,     0)  AS recurrence,
+            coalesce(bf.bruteforce_hits, 0)  AS bruteforce_hits,
+            round(least(100.0,
+                r.distinct_ja4_count * 10
+                + coalesce(rec.recurrence, 0) * 20
+                + least(30.0, log(coalesce(bf.bruteforce_hits, 0) + 1) * 5)
+            ), 1) AS sophistication_score
+        FROM (
+            SELECT
+                replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS ip,
+                distinct_ja4_count
+            FROM mabase_prod.view_host_ip_ja4_rotation
+        ) r
+        LEFT JOIN (
+            SELECT
+                replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS ip,
+                count() AS recurrence
+            FROM mabase_prod.ml_detected_anomalies FINAL
+            GROUP BY ip
+        ) rec ON r.ip = rec.ip
+        LEFT JOIN (
+            SELECT
+                replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS ip,
+                sum(hits) AS bruteforce_hits
+            FROM mabase_prod.view_form_bruteforce_detected
+            GROUP BY ip
+        ) bf ON r.ip = bf.ip
+        ORDER BY sophistication_score DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {"limit": limit})
+        items = []
+        for row in result.result_rows:
+            score = float(row[4] or 0)
+            if score > 80:
+                tier = "APT-like"
+            elif score > 50:
+                tier = "Advanced"
+            elif score > 20:
+                tier = "Automated"
+            else:
+                tier = "Basic"
+            items.append({
+                "ip":                  str(row[0]),
+                "ja4_rotation_count":  int(row[1] or 0),
+                "recurrence":          int(row[2] or 0),
+                "bruteforce_hits":     int(row[3] or 0),
+                "sophistication_score":score,
+                "tier":                tier,
+            })
+        return {"items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/proactive-hunt")
+async def get_proactive_hunt(
+    min_recurrence: int = Query(2, ge=1, description="Récurrence minimale"),
+    min_days: int = Query(2, ge=0, description="Jours d'activité minimum"),
+    limit: int = Query(50, ge=1, le=500),
+):
+    """IPs volant sous le radar : récurrentes mais sous le seuil de détection normal."""
+    try:
+        sql = """
+        SELECT
+            replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS ip,
+            recurrence,
+            worst_score,
+            worst_threat_level,
+            first_seen,
+            last_seen,
+            dateDiff('day', first_seen, last_seen) AS days_active
+        FROM mabase_prod.view_ip_recurrence
+        WHERE recurrence >= %(min_recurrence)s
+          AND abs(worst_score) < 0.5
+          AND dateDiff('day', first_seen, last_seen) >= %(min_days)s
+        ORDER BY recurrence DESC, worst_score ASC
+        LIMIT %(limit)s
+        """
+        result = db.query(sql, {
+            "min_recurrence": min_recurrence,
+            "min_days": min_days,
+            "limit": limit,
+        })
+        items = []
+        for row in result.result_rows:
+            recurrence = int(row[1])
+            worst_score = float(row[2] or 0)
+            days_active = int(row[6] or 0)
+            ratio = recurrence / (worst_score + 0.1)
+            risk = "Évadeur potentiel" if ratio > 10 else "Persistant modéré"
+            items.append({
+                "ip": str(row[0]),
+                "recurrence": recurrence,
+                "worst_score": round(worst_score, 4),
+                "worst_threat_level": str(row[3] or ""),
+                "first_seen": str(row[4]),
+                "last_seen": str(row[5]),
+                "days_active": days_active,
+                "risk_assessment": risk,
+            })
+        return {"items": items, "total": len(items)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

services/dashboard/backend/routes/search.py

@@ -0,0 +1,125 @@
+"""
+Endpoint de recherche globale rapide — utilisé par la barre Cmd+K
+"""
+from fastapi import APIRouter, Query
+from ..database import db
+
+router = APIRouter(prefix="/api/search", tags=["search"])
+
+IP_RE = r"^(\d{1,3}\.){0,3}\d{1,3}$"
+
+
+@router.get("/quick")
+async def quick_search(q: str = Query(..., min_length=1, max_length=100)):
+    """
+    Recherche unifiée sur IPs, JA4, ASN, hosts.
+    Retourne jusqu'à 5 résultats par catégorie.
+    """
+    q = q.strip()
+    pattern = f"%{q}%"
+    results = []
+
+    # ── IPs ──────────────────────────────────────────────────────────────────
+    ip_rows = db.query(
+        """
+        SELECT
+            replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS clean_ip,
+            count()          AS hits,
+            max(detected_at) AS last_seen,
+            any(threat_level) AS threat_level
+        FROM ml_detected_anomalies
+        WHERE ilike(toString(src_ip), %(p)s)
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        GROUP BY clean_ip
+        ORDER BY hits DESC
+        """,
+        {"p": pattern},
+    )
+    for r in ip_rows.result_rows:
+        ip = str(r[0])
+        results.append({
+            "type": "ip",
+            "value": ip,
+            "label": ip,
+            "meta": f"{r[1]} détections · {r[3]}",
+            "url": f"/detections/ip/{ip}",
+            "investigation_url": f"/investigation/{ip}",
+        })
+
+    # ── JA4 fingerprints ─────────────────────────────────────────────────────
+    ja4_rows = db.query(
+        """
+        SELECT
+            ja4,
+            count()          AS hits,
+            uniq(src_ip)     AS unique_ips
+        FROM ml_detected_anomalies
+        WHERE ilike(ja4, %(p)s)
+          AND ja4 != ''
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        GROUP BY ja4
+        ORDER BY hits DESC
+        """,
+        {"p": pattern},
+    )
+    for r in ja4_rows.result_rows:
+        results.append({
+            "type": "ja4",
+            "value": str(r[0]),
+            "label": str(r[0]),
+            "meta": f"{r[1]} détections · {r[2]} IPs",
+            "url": f"/investigation/ja4/{r[0]}",
+        })
+
+    # ── Hosts ─────────────────────────────────────────────────────────────────
+    host_rows = db.query(
+        """
+        SELECT
+            host,
+            count()      AS hits,
+            uniq(src_ip) AS unique_ips
+        FROM ml_detected_anomalies
+        WHERE ilike(host, %(p)s)
+          AND host != ''
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        GROUP BY host
+        ORDER BY hits DESC
+        """,
+        {"p": pattern},
+    )
+    for r in host_rows.result_rows:
+        results.append({
+            "type": "host",
+            "value": str(r[0]),
+            "label": str(r[0]),
+            "meta": f"{r[1]} hits · {r[2]} IPs",
+            "url": f"/detections?search={r[0]}",
+        })
+
+    # ── ASN ───────────────────────────────────────────────────────────────────
+    asn_rows = db.query(
+        """
+        SELECT
+            asn_org,
+            asn_number,
+            count()                       AS hits,
+            uniq(src_ip)                  AS unique_ips
+        FROM ml_detected_anomalies
+        WHERE (ilike(asn_org, %(p)s) OR ilike(asn_number, %(p)s))
+          AND asn_org != '' AND asn_number != ''
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        GROUP BY asn_org, asn_number
+        ORDER BY hits DESC
+        """,
+        {"p": pattern},
+    )
+    for r in asn_rows.result_rows:
+        results.append({
+            "type": "asn",
+            "value": str(r[1]),
+            "label": f"AS{r[1]} — {r[0]}",
+            "meta": f"{r[2]} hits · {r[3]} IPs",
+            "url": f"/detections?asn={r[1]}",
+        })
+
+    return {"query": q, "results": results}

services/dashboard/backend/routes/tcp_spoofing.py

@@ -0,0 +1,223 @@
+"""
+Endpoints pour la détection du TCP spoofing / fingerprinting OS
+
+Approche multi-signal (p0f-style) :
+  - TTL initial estimé  → famille OS (Linux/Mac=64, Windows=128, Cisco/BSD=255)
+  - MSS                 → type de réseau (Ethernet=1460, PPPoE=1452, VPN=1380-1420)
+  - Taille de fenêtre   → signature OS précise
+  - Facteur d'échelle   → affine la version kernel/stack TCP
+
+Détection bots : signatures connues (Masscan/ZMap/Mirai) identifiées par combinaison
+win+scale+mss indépendamment de l'UA.
+"""
+from fastapi import APIRouter, HTTPException, Query
+
+from ..database import db
+from ..services.tcp_fingerprint import (
+    fingerprint_os,
+    detect_spoof,
+    declared_os_from_ua,
+)
+
+router = APIRouter(prefix="/api/tcp-spoofing", tags=["tcp_spoofing"])
+
+
+@router.get("/overview")
+async def get_tcp_spoofing_overview():
+    """Statistiques globales avec fingerprinting multi-signal (TTL + MSS + fenêtre + scale)."""
+    try:
+        sql = """
+        SELECT
+            count()                                                              AS total_entries,
+            uniq(src_ip)                                                         AS unique_ips,
+            countIf(tcp_ttl_raw = 0)                                             AS no_tcp_data,
+            countIf(tcp_ttl_raw > 0)                                             AS with_tcp_data,
+            countIf(tcp_ttl_raw > 0 AND tcp_ttl_raw <= 64)                       AS linux_mac_fp,
+            countIf(tcp_ttl_raw > 64 AND tcp_ttl_raw <= 128)                     AS windows_fp,
+            countIf(tcp_ttl_raw > 128)                                           AS cisco_bsd_fp,
+            countIf(tcp_win_raw = 5808 AND tcp_mss_raw = 1452 AND tcp_scale_raw = 4) AS bot_scanner_fp
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 24 HOUR
+        """
+        result = db.query(sql)
+        row = result.result_rows[0]
+
+        # Distribution TTL (top 15)
+        ttl_sql = """
+        SELECT tcp_ttl_raw AS ttl, count() AS cnt, uniq(src_ip) AS ips
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 24 HOUR AND tcp_ttl_raw > 0
+        GROUP BY ttl ORDER BY cnt DESC
+        """
+        ttl_res = db.query(ttl_sql)
+
+        # Distribution MSS — nouveau signal clé (top 12)
+        mss_sql = """
+        SELECT tcp_mss_raw AS mss, count() AS cnt, uniq(src_ip) AS ips
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 24 HOUR AND tcp_mss_raw > 0
+        GROUP BY mss ORDER BY cnt DESC
+        """
+        mss_res = db.query(mss_sql)
+
+        # Distribution fenêtre (top 10)
+        win_sql = """
+        SELECT tcp_win_raw AS win, count() AS cnt
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 24 HOUR AND tcp_ttl_raw > 0
+        GROUP BY win ORDER BY cnt DESC
+        """
+        win_res = db.query(win_sql)
+
+        return {
+            "total_entries":            int(row[0]),
+            "unique_ips":               int(row[1]),
+            "no_tcp_data":              int(row[2]),
+            "with_tcp_data":            int(row[3]),
+            "linux_mac_fingerprint":    int(row[4]),
+            "windows_fingerprint":      int(row[5]),
+            "cisco_bsd_fingerprint":    int(row[6]),
+            "bot_scanner_fingerprint":  int(row[7]),
+            "ttl_distribution":         [
+                {"ttl": int(r[0]), "count": int(r[1]), "ips": int(r[2])}
+                for r in ttl_res.result_rows
+            ],
+            "mss_distribution":         [
+                {"mss": int(r[0]), "count": int(r[1]), "ips": int(r[2])}
+                for r in mss_res.result_rows
+            ],
+            "window_size_distribution": [
+                {"window_size": int(r[0]), "count": int(r[1])}
+                for r in win_res.result_rows
+            ],
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/list")
+async def get_tcp_spoofing_list(
+    limit:      int  = Query(100, ge=1, le=1000),
+    offset:     int  = Query(0, ge=0),
+    spoof_only: bool = Query(False, description="Retourner uniquement les spoofs/bots confirmés"),
+):
+    """Liste avec fingerprinting multi-signal (TTL + MSS + fenêtre + scale).
+    Inclut les champs enrichis : mss, win_scale, initial_ttl, hop_count, confidence, network_path, is_bot_tool.
+    """
+    try:
+        count_sql = """
+        SELECT count() FROM (
+            SELECT src_ip, ja4
+            FROM mabase_prod.agg_host_ip_ja4_1h
+            WHERE window_start >= now() - INTERVAL 24 HOUR AND tcp_ttl_raw > 0
+            GROUP BY src_ip, ja4
+        )
+        """
+        total = int(db.query(count_sql).result_rows[0][0])
+
+        sql = """
+        SELECT
+            replaceRegexpAll(toString(src_ip), '^::ffff:', '') AS src_ip,
+            ja4,
+            any(tcp_ttl_raw)   AS tcp_ttl,
+            any(tcp_win_raw)   AS tcp_window_size,
+            any(tcp_scale_raw) AS tcp_win_scale,
+            any(tcp_mss_raw)   AS tcp_mss,
+            any(first_ua)      AS first_ua,
+            sum(hits)          AS hits
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 24 HOUR AND tcp_ttl_raw > 0
+        GROUP BY src_ip, ja4
+        ORDER BY hits DESC
+        LIMIT %(limit)s OFFSET %(offset)s
+        """
+        result = db.query(sql, {"limit": limit, "offset": offset})
+        items = []
+        for row in result.result_rows:
+            ip    = str(row[0])
+            ja4   = str(row[1] or "")
+            ttl   = int(row[2] or 0)
+            win   = int(row[3] or 0)
+            scale = int(row[4] or 0)
+            mss   = int(row[5] or 0)
+            ua    = str(row[6] or "")
+            hits  = int(row[7] or 0)
+
+            fp         = fingerprint_os(ttl, win, scale, mss)
+            dec_os     = declared_os_from_ua(ua)
+            spoof_res  = detect_spoof(fp, dec_os)
+
+            if spoof_only and not spoof_res.is_spoof:
+                continue
+
+            items.append({
+                "ip":              ip,
+                "ja4":             ja4,
+                "tcp_ttl":         ttl,
+                "tcp_window_size": win,
+                "tcp_win_scale":   scale,
+                "tcp_mss":         mss,
+                "hits":            hits,
+                "first_ua":        ua,
+                "suspected_os":    fp.os_name,
+                "initial_ttl":     fp.initial_ttl,
+                "hop_count":       fp.hop_count,
+                "confidence":      fp.confidence,
+                "network_path":    fp.network_path,
+                "is_bot_tool":     fp.is_bot_tool,
+                "declared_os":     dec_os,
+                "spoof_flag":      spoof_res.is_spoof,
+                "spoof_reason":    spoof_res.reason,
+            })
+        return {"items": items, "total": total}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@router.get("/matrix")
+async def get_tcp_spoofing_matrix():
+    """Matrice OS suspecté × OS déclaré avec fingerprinting multi-signal."""
+    try:
+        sql = """
+        SELECT
+            any(tcp_ttl_raw)   AS ttl,
+            any(tcp_win_raw)   AS win,
+            any(tcp_scale_raw) AS scale,
+            any(tcp_mss_raw)   AS mss,
+            any(first_ua)      AS ua,
+            count()            AS cnt
+        FROM mabase_prod.agg_host_ip_ja4_1h
+        WHERE window_start >= now() - INTERVAL 24 HOUR AND tcp_ttl_raw > 0
+        GROUP BY src_ip, ja4
+        """
+        result = db.query(sql)
+        counts: dict = {}
+        for row in result.result_rows:
+            ttl   = int(row[0] or 0)
+            win   = int(row[1] or 0)
+            scale = int(row[2] or 0)
+            mss   = int(row[3] or 0)
+            ua    = str(row[4] or "")
+            cnt   = int(row[5] or 1)
+
+            fp        = fingerprint_os(ttl, win, scale, mss)
+            dec_os    = declared_os_from_ua(ua)
+            spoof_res = detect_spoof(fp, dec_os)
+
+            key = (fp.os_name, dec_os, spoof_res.is_spoof, fp.is_bot_tool)
+            counts[key] = counts.get(key, 0) + cnt
+
+        matrix = [
+            {
+                "suspected_os": k[0],
+                "declared_os":  k[1],
+                "count":        v,
+                "is_spoof":     k[2],
+                "is_bot_tool":  k[3],
+            }
+            for k, v in counts.items()
+        ]
+        matrix.sort(key=lambda x: x["count"], reverse=True)
+        return {"matrix": matrix}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

services/dashboard/backend/routes/variability.py

@@ -0,0 +1,706 @@
+"""
+Endpoints pour la variabilité des attributs
+"""
+from fastapi import APIRouter, HTTPException, Query
+from typing import Optional
+from ..database import db
+from ..models import (
+    VariabilityResponse, VariabilityAttributes, AttributeValue, Insight,
+    UserAgentsResponse, UserAgentValue
+)
+
+router = APIRouter(prefix="/api/variability", tags=["variability"])
+
+
+# =============================================================================
+# ROUTES SPÉCIFIQUES (doivent être avant les routes génériques)
+# =============================================================================
+
+@router.get("/{attr_type}/{value:path}/ips", response_model=dict)
+async def get_associated_ips(
+    attr_type: str,
+    value: str,
+    limit: int = Query(100, ge=1, le=1000, description="Nombre maximum d'IPs")
+):
+    """
+    Récupère la liste des IPs associées à un attribut
+    """
+    try:
+        # Mapping des types vers les colonnes
+        type_column_map = {
+            "ip": "src_ip",
+            "ja4": "ja4",
+            "country": "country_code",
+            "asn": "asn_number",
+            "host": "host",
+        }
+
+        if attr_type not in type_column_map:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Type invalide. Types supportés: {', '.join(type_column_map.keys())}"
+            )
+
+        column = type_column_map[attr_type]
+
+        query = f"""
+        SELECT src_ip, count() AS hit_count
+        FROM ml_detected_anomalies
+        WHERE {column} = %(value)s
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        GROUP BY src_ip
+        ORDER BY hit_count DESC
+        LIMIT %(limit)s
+        """
+
+        result = db.query(query, {"value": value, "limit": limit})
+
+        total_hits = sum(row[1] for row in result.result_rows) or 1
+        ips = [
+            {"ip": str(row[0]), "count": row[1], "percentage": round(row[1] * 100.0 / total_hits, 2)}
+            for row in result.result_rows
+        ]
+
+        # Compter le total
+        count_query = f"""
+        SELECT uniq(src_ip) AS total
+        FROM ml_detected_anomalies
+        WHERE {column} = %(value)s
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        """
+
+        count_result = db.query(count_query, {"value": value})
+        total = count_result.result_rows[0][0] if count_result.result_rows else 0
+
+        return {
+            "type": attr_type,
+            "value": value,
+            "ips": ips,
+            "total": total,
+            "showing": len(ips)
+        }
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+@router.get("/{attr_type}/{value:path}/attributes", response_model=dict)
+async def get_associated_attributes(
+    attr_type: str,
+    value: str,
+    target_attr: str = Query(..., description="Type d'attribut à récupérer (user_agents, ja4, countries, asns, hosts)"),
+    limit: int = Query(50, ge=1, le=500, description="Nombre maximum de résultats")
+):
+    """
+    Récupère la liste des attributs associés (ex: User-Agents pour un pays)
+    """
+    try:
+        # Mapping des types vers les colonnes
+        type_column_map = {
+            "ip": "src_ip",
+            "ja4": "ja4",
+            "country": "country_code",
+            "asn": "asn_number",
+            "host": "host",
+        }
+
+        # Mapping des attributs cibles
+        target_column_map = {
+            "user_agents": None,  # handled separately via view_dashboard_entities
+            "ja4": "ja4",
+            "countries": "country_code",
+            "asns": "asn_number",
+            "hosts": "host",
+        }
+
+        if attr_type not in type_column_map:
+            raise HTTPException(status_code=400, detail=f"Type '{attr_type}' invalide")
+
+        if target_attr not in target_column_map:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Attribut cible invalide. Supportés: {', '.join(target_column_map.keys())}"
+            )
+
+        column = type_column_map[attr_type]
+        target_column = target_column_map[target_attr]
+
+        # Pour user_agents: requête via view_dashboard_user_agents
+        # Colonnes: src_ip, ja4, hour, log_date, user_agents, requests
+        if target_column is None:
+            if attr_type == "ip":
+                ua_where = "toString(src_ip) = %(value)s"
+            elif attr_type == "ja4":
+                ua_where = "ja4 = %(value)s"
+            else:
+                # country/asn/host: pivot via ml_detected_anomalies
+                ua_where = f"""toString(src_ip) IN (
+                    SELECT DISTINCT replaceRegexpAll(toString(src_ip), '^::ffff:', '')
+                    FROM ml_detected_anomalies
+                    WHERE {column} = %(value)s AND detected_at >= now() - INTERVAL 24 HOUR
+                )"""
+            ua_q = f"""
+            SELECT ua AS value, sum(requests) AS count,
+                   round(sum(requests) * 100.0 / sum(sum(requests)) OVER (), 2) AS percentage
+            FROM view_dashboard_user_agents
+            ARRAY JOIN user_agents AS ua
+            WHERE {ua_where}
+              AND hour >= now() - INTERVAL 24 HOUR AND ua != ''
+            GROUP BY value ORDER BY count DESC LIMIT %(limit)s
+            """
+            ua_result = db.query(ua_q, {"value": value, "limit": limit})
+            items = [{"value": str(r[0]), "count": r[1], "percentage": round(float(r[2]), 2) if r[2] else 0.0}
+                     for r in ua_result.result_rows]
+            return {"type": attr_type, "value": value, "target": target_attr, "items": items, "total": len(items), "showing": len(items)}
+
+        query = f"""
+        SELECT
+            {target_column} AS value,
+            count() AS count,
+            round(count() * 100.0 / sum(count()) OVER (), 2) AS percentage
+        FROM ml_detected_anomalies
+        WHERE {column} = %(value)s
+          AND {target_column} != '' AND {target_column} IS NOT NULL
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        GROUP BY value
+        ORDER BY count DESC
+        LIMIT %(limit)s
+        """
+
+        result = db.query(query, {"value": value, "limit": limit})
+
+        items = [
+            {
+                "value": str(row[0]),
+                "count": row[1],
+                "percentage": round(float(row[2]), 2) if row[2] else 0.0
+            }
+            for row in result.result_rows
+        ]
+
+        # Compter le total
+        count_query = f"""
+        SELECT uniq({target_column}) AS total
+        FROM ml_detected_anomalies
+        WHERE {column} = %(value)s
+          AND {target_column} != '' AND {target_column} IS NOT NULL
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        """
+
+        count_result = db.query(count_query, {"value": value})
+        total = count_result.result_rows[0][0] if count_result.result_rows else 0
+
+        return {
+            "type": attr_type,
+            "value": value,
+            "target": target_attr,
+            "items": items,
+            "total": total,
+            "showing": len(items)
+        }
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+@router.get("/{attr_type}/{value:path}/user_agents", response_model=UserAgentsResponse)
+async def get_user_agents(
+    attr_type: str,
+    value: str,
+    limit: int = Query(100, ge=1, le=500, description="Nombre maximum de user-agents")
+):
+    """
+    Récupère la liste des User-Agents associés à un attribut (IP, JA4, pays, etc.)
+    Les données sont récupérées depuis la vue materialisée view_dashboard_user_agents
+    """
+    try:
+        # Mapping des types vers les colonnes
+        type_column_map = {
+            "ip": "src_ip",
+            "ja4": "ja4",
+            "country": "country_code",
+            "asn": "asn_number",
+            "host": "host",
+        }
+
+        if attr_type not in type_column_map:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Type invalide. Types supportés: {', '.join(type_column_map.keys())}"
+            )
+
+        column = type_column_map[attr_type]
+
+        # view_dashboard_user_agents colonnes: src_ip, ja4, hour, log_date, user_agents, requests
+        if attr_type == "ip":
+            where = "toString(src_ip) = %(value)s"
+            params: dict = {"value": value, "limit": limit}
+        elif attr_type == "ja4":
+            where = "ja4 = %(value)s"
+            params = {"value": value, "limit": limit}
+        else:
+            # country / asn / host: pivot via ml_detected_anomalies → IPs connus → vue par src_ip
+            ml_col = {"country": "country_code", "asn": "asn_number", "host": "host"}[attr_type]
+            where = f"""toString(src_ip) IN (
+                SELECT DISTINCT replaceRegexpAll(toString(src_ip), '^::ffff:', '')
+                FROM ml_detected_anomalies
+                WHERE {ml_col} = %(value)s
+                  AND detected_at >= now() - INTERVAL 24 HOUR
+            )"""
+            params = {"value": value, "limit": limit}
+
+        query = f"""
+        SELECT
+            ua AS user_agent,
+            sum(requests) AS count,
+            round(sum(requests) * 100.0 / sum(sum(requests)) OVER (), 2) AS percentage,
+            min(log_date) AS first_seen,
+            max(log_date) AS last_seen
+        FROM view_dashboard_user_agents
+        ARRAY JOIN user_agents AS ua
+        WHERE {where}
+          AND hour >= now() - INTERVAL 24 HOUR
+          AND ua != ''
+        GROUP BY user_agent
+        ORDER BY count DESC
+        LIMIT %(limit)s
+        """
+        result = db.query(query, params)
+
+        count_query = f"""
+        SELECT uniqExact(ua) AS total
+        FROM view_dashboard_user_agents
+        ARRAY JOIN user_agents AS ua
+        WHERE {where}
+          AND hour >= now() - INTERVAL 24 HOUR
+          AND ua != ''
+        """
+        count_result = db.query(count_query, params)
+
+        user_agents = [
+            UserAgentValue(
+                value=str(row[0]),
+                count=row[1] or 0,
+                percentage=round(float(row[2]), 2) if row[2] else 0.0,
+                first_seen=row[3] if len(row) > 3 and row[3] else None,
+                last_seen=row[4] if len(row) > 4 and row[4] else None,
+            )
+            for row in result.result_rows
+        ]
+
+        total = count_result.result_rows[0][0] if count_result.result_rows else 0
+
+        return {
+            "type": attr_type,
+            "value": value,
+            "user_agents": user_agents,
+            "total": total,
+            "showing": len(user_agents)
+        }
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")
+
+
+# =============================================================================
+# ROUTE GÉNÉRIQUE (doit être en dernier)
+# =============================================================================
+
+
+def get_attribute_value(row, count_idx: int, percentage_idx: int, 
+                        first_seen_idx: Optional[int] = None,
+                        last_seen_idx: Optional[int] = None,
+                        threat_idx: Optional[int] = None,
+                        unique_ips_idx: Optional[int] = None) -> AttributeValue:
+    """Helper pour créer un AttributeValue depuis une ligne ClickHouse"""
+    return AttributeValue(
+        value=str(row[0]),
+        count=row[count_idx] or 0,
+        percentage=round(float(row[percentage_idx]), 2) if row[percentage_idx] else 0.0,
+        first_seen=row[first_seen_idx] if first_seen_idx is not None and len(row) > first_seen_idx else None,
+        last_seen=row[last_seen_idx] if last_seen_idx is not None and len(row) > last_seen_idx else None,
+        threat_levels=_parse_threat_levels(row[threat_idx]) if threat_idx is not None and len(row) > threat_idx and row[threat_idx] else None,
+        unique_ips=row[unique_ips_idx] if unique_ips_idx is not None and len(row) > unique_ips_idx else None,
+        primary_threat=_get_primary_threat(row[threat_idx]) if threat_idx is not None and len(row) > threat_idx and row[threat_idx] else None
+    )
+
+
+def _parse_threat_levels(threat_str: str) -> dict:
+    """Parse une chaîne de type 'CRITICAL:5,HIGH:10' en dict"""
+    if not threat_str:
+        return {}
+    result = {}
+    for part in str(threat_str).split(','):
+        if ':' in part:
+            level, count = part.strip().split(':')
+            result[level.strip()] = int(count.strip())
+    return result
+
+
+def _get_primary_threat(threat_str: str) -> str:
+    """Retourne le niveau de menace principal"""
+    if not threat_str:
+        return ""
+    levels_order = ["CRITICAL", "HIGH", "MEDIUM", "LOW"]
+    for level in levels_order:
+        if level in str(threat_str):
+            return level
+    return ""
+
+
+def _generate_insights(attr_type: str, value: str, attributes: VariabilityAttributes, 
+                       total_detections: int, unique_ips: int) -> list:
+    """Génère des insights basés sur les données de variabilité"""
+    insights = []
+    
+    # User-Agent insights
+    if len(attributes.user_agents) > 1:
+        insights.append(Insight(
+            type="warning",
+            message=f"{len(attributes.user_agents)} User-Agents différents → Possible rotation/obfuscation"
+        ))
+    
+    # JA4 insights
+    if len(attributes.ja4) > 1:
+        insights.append(Insight(
+            type="warning",
+            message=f"{len(attributes.ja4)} JA4 fingerprints différents → Possible rotation de fingerprint"
+        ))
+    
+    # IP insights (pour les sélections non-IP)
+    if attr_type != "ip" and unique_ips > 10:
+        insights.append(Insight(
+            type="info",
+            message=f"{unique_ips} IPs différentes associées → Possible infrastructure distribuée"
+        ))
+    
+    # ASN insights
+    if len(attributes.asns) == 1 and attributes.asns[0].value:
+        asn_label_lower = ""
+        if attr_type == "asn":
+            asn_label_lower = value.lower()
+        # Vérifier si c'est un ASN de hosting/cloud
+        hosting_keywords = ["ovh", "amazon", "aws", "google", "azure", "digitalocean", "linode", "vultr"]
+        if any(kw in (attributes.asns[0].value or "").lower() for kw in hosting_keywords):
+            insights.append(Insight(
+                type="warning",
+                message="ASN de type hosting/cloud → Souvent utilisé pour des bots"
+            ))
+    
+    # Country insights
+    if len(attributes.countries) > 5:
+        insights.append(Insight(
+            type="info",
+            message=f"Présent dans {len(attributes.countries)} pays → Distribution géographique large"
+        ))
+    
+    # Threat level insights
+    critical_count = 0
+    high_count = 0
+    for tl in attributes.threat_levels:
+        if tl.value == "CRITICAL":
+            critical_count = tl.count
+        elif tl.value == "HIGH":
+            high_count = tl.count
+    
+    if critical_count > total_detections * 0.3:
+        insights.append(Insight(
+            type="warning",
+            message=f"{round(critical_count * 100 / total_detections)}% de détections CRITICAL → Menace sévère"
+        ))
+    elif high_count > total_detections * 0.5:
+        insights.append(Insight(
+            type="info",
+            message=f"{round(high_count * 100 / total_detections)}% de détections HIGH → Activité suspecte"
+        ))
+    
+    return insights
+
+
+@router.get("/{attr_type}/{value:path}", response_model=VariabilityResponse)
+async def get_variability(attr_type: str, value: str):
+    """
+    Récupère la variabilité des attributs associés à une valeur
+    
+    attr_type: ip, ja4, country, asn, host, user_agent
+    value: la valeur à investiguer
+    """
+    try:
+        # Mapping des types vers les colonnes ClickHouse
+        type_column_map = {
+            "ip": "src_ip",
+            "ja4": "ja4",
+            "country": "country_code",
+            "asn": "asn_number",
+            "host": "host",
+            "user_agent": "header_user_agent"
+        }
+        
+        if attr_type not in type_column_map:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Type invalide. Types supportés: {', '.join(type_column_map.keys())}"
+            )
+        
+        column = type_column_map[attr_type]
+        
+        # Requête principale - Récupère toutes les détections pour cette valeur
+        # On utilise toStartOfHour pour le timeseries et on évite header_user_agent si inexistant
+        base_query = f"""
+        SELECT *
+        FROM (
+            SELECT
+                detected_at,
+                src_ip,
+                ja4,
+                host,
+                '' AS user_agent,
+                country_code,
+                asn_number,
+                asn_org,
+                threat_level,
+                model_name,
+                anomaly_score
+            FROM ml_detected_anomalies
+            WHERE {column} = %(value)s
+              AND detected_at >= now() - INTERVAL 24 HOUR
+        )
+        """
+        
+        # Stats globales
+        stats_query = f"""
+        SELECT
+            count() AS total_detections,
+            uniq(src_ip) AS unique_ips,
+            min(detected_at) AS first_seen,
+            max(detected_at) AS last_seen
+        FROM ml_detected_anomalies
+        WHERE {column} = %(value)s
+          AND detected_at >= now() - INTERVAL 24 HOUR
+        """
+        
+        stats_result = db.query(stats_query, {"value": value})
+        
+        if not stats_result.result_rows or stats_result.result_rows[0][0] == 0:
+            raise HTTPException(status_code=404, detail="Aucune donnée trouvée")
+        
+        stats_row = stats_result.result_rows[0]
+        total_detections = stats_row[0]
+        unique_ips = stats_row[1]
+        first_seen = stats_row[2]
+        last_seen = stats_row[3]
+        
+        # User-Agents depuis http_logs pour des comptes exacts par requête
+        # (view_dashboard_user_agents déduplique par heure, ce qui sous-compte les hits)
+        _ua_params: dict = {"value": value}
+        if attr_type == "ip":
+            _ua_logs_where = "src_ip = toIPv4(%(value)s)"
+            ua_query_simple = f"""
+            SELECT
+                header_user_agent AS user_agent,
+                count() AS count,
+                round(count() * 100.0 / (
+                    SELECT count() FROM mabase_prod.http_logs
+                    WHERE {_ua_logs_where} AND time >= now() - INTERVAL 24 HOUR
+                ), 2) AS percentage,
+                min(time) AS first_seen,
+                max(time) AS last_seen
+            FROM mabase_prod.http_logs
+            WHERE {_ua_logs_where}
+              AND time >= now() - INTERVAL 24 HOUR
+              AND header_user_agent != '' AND header_user_agent IS NOT NULL
+            GROUP BY user_agent
+            ORDER BY count DESC
+            """
+            ua_result = db.query(ua_query_simple, _ua_params)
+            user_agents = [get_attribute_value(row, 1, 2, 3, 4) for row in ua_result.result_rows]
+        elif attr_type == "ja4":
+            _ua_logs_where = "ja4 = %(value)s"
+            ua_query_simple = f"""
+            SELECT
+                header_user_agent AS user_agent,
+                count() AS count,
+                round(count() * 100.0 / (
+                    SELECT count() FROM mabase_prod.http_logs
+                    WHERE {_ua_logs_where} AND time >= now() - INTERVAL 24 HOUR
+                ), 2) AS percentage,
+                min(time) AS first_seen,
+                max(time) AS last_seen
+            FROM mabase_prod.http_logs
+            WHERE {_ua_logs_where}
+              AND time >= now() - INTERVAL 24 HOUR
+              AND header_user_agent != '' AND header_user_agent IS NOT NULL
+            GROUP BY user_agent
+            ORDER BY count DESC
+            """
+            ua_result = db.query(ua_query_simple, _ua_params)
+            user_agents = [get_attribute_value(row, 1, 2, 3, 4) for row in ua_result.result_rows]
+        else:
+            # country / asn / host: pivot via ml_detected_anomalies → IPs, puis view UA
+            _ua_where = f"""toString(src_ip) IN (
+                SELECT DISTINCT replaceRegexpAll(toString(src_ip), '^::ffff:', '')
+                FROM ml_detected_anomalies
+                WHERE {column} = %(value)s AND detected_at >= now() - INTERVAL 24 HOUR
+            )"""
+            ua_query_simple = f"""
+            SELECT
+                ua AS user_agent,
+                sum(requests) AS count,
+                round(sum(requests) * 100.0 / sum(sum(requests)) OVER (), 2) AS percentage,
+                min(log_date) AS first_seen,
+                max(log_date) AS last_seen
+            FROM view_dashboard_user_agents
+            ARRAY JOIN user_agents AS ua
+            WHERE {_ua_where}
+              AND hour >= now() - INTERVAL 24 HOUR
+              AND ua != ''
+            GROUP BY user_agent
+            ORDER BY count DESC
+            """
+            ua_result = db.query(ua_query_simple, _ua_params)
+            user_agents = [get_attribute_value(row, 1, 2, 3, 4) for row in ua_result.result_rows]
+        
+        # JA4 fingerprints
+        ja4_query = f"""
+        SELECT
+            ja4,
+            count() AS count,
+            round(count() * 100.0 / (SELECT count() FROM ({base_query})), 2) AS percentage,
+            min(detected_at) AS first_seen,
+            max(detected_at) AS last_seen
+        FROM ({base_query})
+        WHERE ja4 != '' AND ja4 IS NOT NULL
+        GROUP BY ja4
+        ORDER BY count DESC
+        """
+        
+        ja4_result = db.query(ja4_query, {"value": value})
+        ja4s = [get_attribute_value(row, 1, 2, 3, 4) for row in ja4_result.result_rows]
+        
+        # Pays
+        country_query = f"""
+        SELECT
+            country_code,
+            count() AS count,
+            round(count() * 100.0 / (SELECT count() FROM ({base_query})), 2) AS percentage
+        FROM ({base_query})
+        WHERE country_code != '' AND country_code IS NOT NULL
+        GROUP BY country_code
+        ORDER BY count DESC
+        """
+        
+        country_result = db.query(country_query, {"value": value})
+        countries = [get_attribute_value(row, 1, 2) for row in country_result.result_rows]
+        
+        # ASN
+        asn_query = f"""
+        SELECT
+            concat('AS', toString(asn_number), ' - ', asn_org) AS asn_display,
+            asn_number,
+            count() AS count,
+            round(count() * 100.0 / (SELECT count() FROM ({base_query})), 2) AS percentage
+        FROM ({base_query})
+        WHERE asn_number != '' AND asn_number IS NOT NULL AND asn_number != '0'
+        GROUP BY asn_display, asn_number
+        ORDER BY count DESC
+        """
+        
+        asn_result = db.query(asn_query, {"value": value})
+        asns = [
+            AttributeValue(
+                value=str(row[0]),
+                count=row[2] or 0,
+                percentage=round(float(row[3]), 2) if row[3] else 0.0
+            )
+            for row in asn_result.result_rows
+        ]
+        
+        # Hosts
+        host_query = f"""
+        SELECT
+            host,
+            count() AS count,
+            round(count() * 100.0 / (SELECT count() FROM ({base_query})), 2) AS percentage
+        FROM ({base_query})
+        WHERE host != '' AND host IS NOT NULL
+        GROUP BY host
+        ORDER BY count DESC
+        """
+        
+        host_result = db.query(host_query, {"value": value})
+        hosts = [get_attribute_value(row, 1, 2) for row in host_result.result_rows]
+        
+        # Threat levels
+        threat_query = f"""
+        SELECT
+            threat_level,
+            count() AS count,
+            round(count() * 100.0 / (SELECT count() FROM ({base_query})), 2) AS percentage
+        FROM ({base_query})
+        WHERE threat_level != '' AND threat_level IS NOT NULL
+        GROUP BY threat_level
+        ORDER BY 
+            CASE threat_level
+                WHEN 'CRITICAL' THEN 1
+                WHEN 'HIGH' THEN 2
+                WHEN 'MEDIUM' THEN 3
+                WHEN 'LOW' THEN 4
+                ELSE 5
+            END
+        """
+        
+        threat_result = db.query(threat_query, {"value": value})
+        threat_levels = [get_attribute_value(row, 1, 2) for row in threat_result.result_rows]
+        
+        # Model names
+        model_query = f"""
+        SELECT
+            model_name,
+            count() AS count,
+            round(count() * 100.0 / (SELECT count() FROM ({base_query})), 2) AS percentage
+        FROM ({base_query})
+        WHERE model_name != '' AND model_name IS NOT NULL
+        GROUP BY model_name
+        ORDER BY count DESC
+        """
+        
+        model_result = db.query(model_query, {"value": value})
+        model_names = [get_attribute_value(row, 1, 2) for row in model_result.result_rows]
+        
+        # Construire la réponse
+        attributes = VariabilityAttributes(
+            user_agents=user_agents,
+            ja4=ja4s,
+            countries=countries,
+            asns=asns,
+            hosts=hosts,
+            threat_levels=threat_levels,
+            model_names=model_names
+        )
+        
+        # Générer les insights
+        insights = _generate_insights(attr_type, value, attributes, total_detections, unique_ips)
+        
+        return VariabilityResponse(
+            type=attr_type,
+            value=value,
+            total_detections=total_detections,
+            unique_ips=unique_ips,
+            date_range={
+                "first_seen": first_seen,
+                "last_seen": last_seen
+            },
+            attributes=attributes,
+            insights=insights
+        )
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Erreur: {str(e)}")

services/dashboard/backend/services/clustering_engine.py

@@ -0,0 +1,493 @@
+"""
+Moteur de clustering K-means++ multi-métriques (numpy + scipy vectorisé).
+
+Ref:
+  Arthur & Vassilvitskii (2007) — k-means++: The Advantages of Careful Seeding
+  scipy.spatial.ConvexHull     — enveloppe convexe (Graham/Qhull)
+  sklearn-style API             — centroids, labels_, inertia_
+
+Features (31 dimensions, normalisées [0,1]) :
+  0  ttl_n              : TTL initial normalisé
+  1  mss_n              : MSS normalisé → type réseau
+  2  scale_n            : facteur de mise à l'échelle TCP
+  3  win_n              : fenêtre TCP normalisée
+  4  score_n            : score anomalie ML (abs)
+  5  velocity_n         : vélocité de requêtes (log1p)
+  6  fuzzing_n          : index de fuzzing (log1p)
+  7  headless_n         : ratio sessions headless
+  8  post_n             : ratio POST/total
+  9  ip_id_zero_n       : ratio IP-ID=0 (Linux/spoofé)
+  10 entropy_n          : entropie temporelle
+  11 browser_n          : score navigateur moderne
+  12 alpn_n             : mismatch ALPN/protocole
+  13 alpn_absent_n      : ratio ALPN absent
+  14 h2_n               : efficacité H2 multiplexing (log1p)
+  15 hdr_conf_n         : confiance ordre headers
+  16 ua_ch_n            : mismatch User-Agent-Client-Hints
+  17 asset_n            : ratio assets statiques
+  18 direct_n           : ratio accès directs
+  19 ja4_div_n          : diversité JA4 (log1p)
+  20 ua_rot_n           : UA rotatif (booléen)
+  21 country_risk_n     : risque pays source (CN/RU/KP → 1.0, US/DE/FR → 0.0)
+  22 asn_cloud_n        : hébergeur cloud/CDN/VPN (Cloudflare/AWS/OVH → 1.0)
+  23 hdr_accept_lang_n  : présence header Accept-Language (0=absent=bot-like)
+  24 hdr_encoding_n     : présence header Accept-Encoding  (0=absent=bot-like)
+  25 hdr_sec_fetch_n    : présence headers Sec-Fetch-*     (1=navigateur réel)
+  26 hdr_count_n        : nombre de headers HTTP normalisé (3=bot, 15=browser)
+  27 hfp_popular_n      : popularité du fingerprint headers (log-normalisé)
+                          fingerprint rare = suspect ; très populaire = browser légitime
+  28 hfp_rotating_n     : rotation de fingerprint (distinct_header_orders)
+                          plusieurs fingerprints distincts → bot en rotation
+  29 hfp_cookie_n       : présence header Cookie (engagement utilisateur réel)
+  30 hfp_referer_n      : présence header Referer (navigation HTTP normale)
+"""
+from __future__ import annotations
+
+import math
+import logging
+import numpy as np
+from dataclasses import dataclass, field
+from scipy.spatial import ConvexHull
+
+log = logging.getLogger(__name__)
+
+# ─── Encodage pays (risque source) ───────────────────────────────────────────
+# Source: MISP threat intel, Spamhaus DROP list, géographie offensive connue
+
+_COUNTRY_RISK: dict[str, float] = {
+    # Très haut risque : infrastructure offensive documentée
+    "CN": 1.0, "RU": 1.0, "KP": 1.0, "IR": 1.0,
+    "BY": 0.9, "SY": 0.9, "CU": 0.8,
+    # Haut risque : transit/hébergement permissif, bulletproof hosters
+    "HK": 0.75, "VN": 0.7, "UA": 0.65,
+    "RO": 0.6,  "PK": 0.6, "NG": 0.6,
+    "BG": 0.55, "TR": 0.55, "BR": 0.5,
+    "TH": 0.5,  "IN": 0.45, "ID": 0.45,
+    # Risque faible : pays à faible tolérance envers activité malveillante
+    "US": 0.1, "DE": 0.1, "FR": 0.1, "GB": 0.1,
+    "CA": 0.1, "JP": 0.1, "AU": 0.1, "NL": 0.15,
+    "CH": 0.1, "SE": 0.1, "NO": 0.1, "DK": 0.1,
+    "FI": 0.1, "AT": 0.1, "BE": 0.1, "IT": 0.15,
+    "SG": 0.3, "TW": 0.2, "KR": 0.2, "RS": 0.4,
+}
+_DEFAULT_COUNTRY_RISK = 0.35  # pays inconnu → risque modéré
+
+
+def country_risk(cc: str | None) -> float:
+    """Score de risque [0,1] d'un code pays ISO-3166."""
+    return _COUNTRY_RISK.get((cc or "").upper(), _DEFAULT_COUNTRY_RISK)
+
+
+# ─── Encodage ASN (type d'infrastructure) ────────────────────────────────────
+# Cloud/CDN/hosting → fort corrélé avec scanners automatisés et bots
+
+_ASN_CLOUD_KEYWORDS = [
+    # Hyperscalers
+    "amazon", "aws", "google", "microsoft", "azure", "alibaba", "tencent", "huawei",
+    # CDN / edge
+    "cloudflare", "akamai", "fastly", "cloudfront", "incapsula", "imperva",
+    "sucuri", "stackpath", "keycdn",
+    # Hébergeurs
+    "ovh", "hetzner", "digitalocean", "vultr", "linode", "akamai-linode",
+    "leaseweb", "choopa", "packet", "equinix", "serverius", "combahton",
+    "m247", "b2 net", "hostinger", "contabo",
+    # Bulletproof / transit permissif connus
+    "hwclouds", "multacom", "psychz", "serverius", "colocrossing",
+    "frantech", "sharktech", "tzulo",
+    # VPN / proxy commerciaux
+    "nordvpn", "expressvpn", "mullvad", "protonvpn", "surfshark",
+    "privateinternetaccess", "pia ", "cyberghost", "hotspot shield",
+    "ipvanish", "hide.me",
+    # Bots search engines / crawlers
+    "facebook", "meta ", "twitter", "linkedin", "semrush", "ahrefs",
+    "majestic", "moz ", "babbar", "sistrix", "criteo", "peer39",
+]
+
+
+def asn_cloud_score(asn_org: str | None) -> float:
+    """
+    Score [0,1] : 1.0 = cloud/CDN/hébergement/VPN confirmé.
+    Correspond à une infrastructure typiquement utilisée par les bots.
+    """
+    if not asn_org:
+        return 0.2  # inconnu → légèrement suspect
+    s = asn_org.lower()
+    for kw in _ASN_CLOUD_KEYWORDS:
+        if kw in s:
+            return 1.0
+    return 0.0
+
+
+# ─── Définition des features ──────────────────────────────────────────────────
+
+FEATURES: list[tuple[str, str, object]] = [
+    # TCP stack
+    ("ttl",           "TTL Initial",           lambda v: min(1.0, (v or 0) / 255.0)),
+    ("mss",           "MSS Réseau",            lambda v: min(1.0, (v or 0) / 1460.0)),
+    ("scale",         "Scale TCP",             lambda v: min(1.0, (v or 0) / 14.0)),
+    ("win",           "Fenêtre TCP",           lambda v: min(1.0, (v or 0) / 65535.0)),
+    # Anomalie ML
+    ("avg_velocity",  "Vélocité (rps)",        lambda v: min(1.0, math.log1p(float(v or 0)) / math.log1p(100))),    ("avg_fuzzing",   "Fuzzing",               lambda v: min(1.0, math.log1p(float(v or 0)) / math.log1p(300))),
+    ("pct_headless",  "Headless",              lambda v: min(1.0, float(v or 0))),
+    ("avg_post",      "Ratio POST",            lambda v: min(1.0, float(v or 0))),
+    # IP-ID
+    ("ip_id_zero",    "IP-ID Zéro",           lambda v: min(1.0, float(v or 0))),
+    # Temporel
+    ("entropy",       "Entropie Temporelle",   lambda v: min(1.0, math.log1p(float(v or 0)) / math.log1p(10))),
+    # Navigateur
+    ("browser_score", "Score Navigateur",      lambda v: min(1.0, float(v or 0) / 50.0)),
+    # TLS / Protocole
+    ("alpn_mismatch", "ALPN Mismatch",         lambda v: min(1.0, float(v or 0))),
+    ("alpn_missing",  "ALPN Absent",           lambda v: min(1.0, float(v or 0))),
+    ("h2_eff",        "H2 Multiplexing",       lambda v: min(1.0, math.log1p(float(v or 0)) / math.log1p(20))),
+    ("hdr_conf",      "Ordre Headers",         lambda v: min(1.0, float(v or 0))),
+    ("ua_ch_mismatch","UA-CH Mismatch",        lambda v: min(1.0, float(v or 0))),
+    # Comportement HTTP
+    ("asset_ratio",   "Ratio Assets",          lambda v: min(1.0, float(v or 0))),
+    ("direct_ratio",  "Accès Direct",          lambda v: min(1.0, float(v or 0))),
+    # Diversité JA4
+    ("ja4_count",     "Diversité JA4",         lambda v: min(1.0, math.log1p(float(v or 0)) / math.log1p(30))),
+    # UA rotatif
+    ("ua_rotating",   "UA Rotatif",            lambda v: 1.0 if float(v or 0) > 0 else 0.0),
+    # ── Géographie & infrastructure (nouvelles features) ──────────────────
+    ("country",           "Risque Pays",           lambda v: country_risk(str(v) if v else None)),
+    ("asn_org",           "Hébergeur Cloud/VPN",   lambda v: asn_cloud_score(str(v) if v else None)),
+    # ── Headers HTTP (présence / profil de la requête) ────────────────────
+    # Absence d'Accept-Language ou Accept-Encoding = fort signal bot (bots simples l'omettent)
+    # Sec-Fetch-* = exclusif aux navigateurs réels (fetch metadata)
+    ("hdr_accept_lang",   "Accept-Language",       lambda v: min(1.0, float(v or 0))),
+    ("hdr_has_encoding",  "Accept-Encoding",       lambda v: 1.0 if float(v or 0) > 0 else 0.0),
+    ("hdr_has_sec_fetch", "Sec-Fetch Headers",     lambda v: 1.0 if float(v or 0) > 0 else 0.0),
+    ("hdr_count_raw",     "Nb Headers",            lambda v: min(1.0, float(v or 0) / 20.0)),
+    # ── Fingerprint HTTP Headers (agg_header_fingerprint_1h) ──────────────
+    # header_order_shared_count : nb d'IPs partageant ce fingerprint
+    #   élevé → populaire → browser légitime (normalisé log1p / log1p(500000))
+    ("hfp_shared_count",    "FP Popularité",       lambda v: min(1.0, math.log1p(float(v or 0)) / math.log1p(500_000))),
+    # distinct_header_orders : nb de fingerprints distincts pour cette IP
+    #   élevé → rotation de fingerprint → bot (normalisé log1p / log1p(10))
+    ("hfp_distinct_orders", "FP Rotation",         lambda v: min(1.0, math.log1p(float(v or 0)) / math.log1p(10))),
+    # Cookie et Referer : signaux de navigation légitime
+    ("hfp_cookie",          "Cookie Présent",      lambda v: min(1.0, float(v or 0))),
+    ("hfp_referer",         "Referer Présent",     lambda v: min(1.0, float(v or 0))),
+]
+
+FEATURE_KEYS  = [f[0] for f in FEATURES]
+FEATURE_NAMES = [f[1] for f in FEATURES]
+FEATURE_NORMS = [f[2] for f in FEATURES]
+N_FEATURES    = len(FEATURES)
+
+
+# ─── Construction du vecteur de features ─────────────────────────────────────
+
+def build_feature_vector(row: dict) -> list[float]:
+    """Construit le vecteur normalisé [0,1]^23 depuis un dict SQL."""
+    return [norm(row.get(key, 0)) for key, _, norm in FEATURES]
+
+
+# ─── Standardisation z-score ──────────────────────────────────────────────────
+
+def standardize(X: np.ndarray) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    Z-score standardisation : chaque feature est centrée et mise à l'échelle
+    par sa déviation standard.
+
+    Ref: Bishop (2006) PRML §9.1 — preprocessing recommandé pour K-means.
+
+    Retourne (X_std, mean, std) pour pouvoir projeter de nouveaux points.
+    """
+    mean = X.mean(axis=0)
+    std  = X.std(axis=0)
+    std[std < 1e-8] = 1.0  # évite la division par zéro pour features constantes
+    return (X - mean) / std, mean, std
+
+
+# ─── K-means++ vectorisé (numpy) ─────────────────────────────────────────────
+
+@dataclass
+class KMeansResult:
+    centroids: np.ndarray        # (k, n_features)
+    labels: np.ndarray           # (n_points,)  int32
+    inertia: float
+    n_iter: int
+
+
+def kmeans_pp(X: np.ndarray, k: int, max_iter: int = 60, n_init: int = 3,
+              seed: int = 42) -> KMeansResult:
+    """
+    K-means++ entièrement vectorisé avec numpy.
+    n_init exécutions, meilleure inertie conservée.
+    """
+    rng = np.random.default_rng(seed)
+    n, d = X.shape
+    best: KMeansResult | None = None
+
+    for _ in range(n_init):
+        # ── Initialisation K-means++ ──────────────────────────────────────
+        centers = [X[rng.integers(n)].copy()]
+        for _ in range(k - 1):
+            D = _min_sq_dist(X, np.array(centers))
+            # Garantit des probabilités non-négatives (erreurs float, points dupliqués)
+            D = np.clip(D, 0.0, None)
+            total = D.sum()
+            if total < 1e-12:
+                # Tous les points sont confondus — tirage aléatoire
+                centers.append(X[rng.integers(n)].copy())
+            else:
+                probs = D / total
+                centers.append(X[rng.choice(n, p=probs)].copy())
+        centers_arr = np.array(centers)  # (k, d)
+
+        # ── Iterations ───────────────────────────────────────────────────
+        labels = np.zeros(n, dtype=np.int32)
+        for it in range(max_iter):
+            # Assignation vectorisée : (n, k) distance²
+            dists = _sq_dists(X, centers_arr)  # (n, k)
+            new_labels = np.argmin(dists, axis=1).astype(np.int32)
+
+            if it > 0 and np.all(new_labels == labels):
+                break  # convergence
+            labels = new_labels
+
+            # Mise à jour des centroïdes
+            for j in range(k):
+                mask = labels == j
+                if mask.any():
+                    centers_arr[j] = X[mask].mean(axis=0)
+
+        inertia = float(np.sum(np.min(_sq_dists(X, centers_arr), axis=1)))
+        result = KMeansResult(centers_arr, labels, inertia, it + 1)
+        if best is None or inertia < best.inertia:
+            best = result
+
+    return best  # type: ignore[return-value]
+
+
+def _sq_dists(X: np.ndarray, C: np.ndarray) -> np.ndarray:
+    """Distance² entre chaque point de X et chaque centroïde de C. O(n·k·d)."""
+    # ||x - c||² = ||x||² + ||c||² - 2·x·cᵀ
+    X2 = np.sum(X ** 2, axis=1, keepdims=True)  # (n, 1)
+    C2 = np.sum(C ** 2, axis=1, keepdims=True).T  # (1, k)
+    return X2 + C2 - 2.0 * X @ C.T              # (n, k)
+
+
+def _min_sq_dist(X: np.ndarray, C: np.ndarray) -> np.ndarray:
+    """Distance² minimale de chaque point aux centroïdes existants."""
+    return np.min(_sq_dists(X, C), axis=1)
+
+
+# ─── PCA 2D (numpy) ──────────────────────────────────────────────────────────
+
+def pca_2d(X: np.ndarray) -> np.ndarray:
+    """
+    PCA-2D vectorisée. Retourne les coordonnées normalisées [0,1] × [0,1].
+    """
+    mean = X.mean(axis=0)
+    Xc = X - mean
+    # Power iteration pour les 2 premières composantes
+    rng = np.random.default_rng(0)
+    v1 = _power_iter(Xc, rng.standard_normal(Xc.shape[1]))
+    proj1 = Xc @ v1
+    # Déflation (Hotelling)
+    Xc2 = Xc - np.outer(proj1, v1)
+    v2 = _power_iter(Xc2, rng.standard_normal(Xc.shape[1]))
+    proj2 = Xc2 @ v2
+
+    coords = np.column_stack([proj1, proj2])
+    # Normalisation [0,1]
+    mn, mx = coords.min(axis=0), coords.max(axis=0)
+    rng_ = mx - mn
+    rng_[rng_ == 0] = 1.0
+    return (coords - mn) / rng_
+
+
+def _power_iter(X: np.ndarray, v: np.ndarray, n_iter: int = 30) -> np.ndarray:
+    """Power iteration : trouve le premier vecteur propre de XᵀX."""
+    for _ in range(n_iter):
+        v = X.T @ (X @ v)
+        norm = np.linalg.norm(v)
+        if norm < 1e-12:
+            break
+        v /= norm
+    return v
+
+
+# ─── Enveloppe convexe (hull) par cluster ────────────────────────────────────
+
+def compute_hulls(coords_2d: np.ndarray, labels: np.ndarray,
+                  k: int, min_pts: int = 4) -> dict[int, list[list[float]]]:
+    """
+    Calcule l'enveloppe convexe (convex hull) des points PCA pour chaque cluster.
+    Retourne {cluster_idx: [[x,y], ...]} (polygone fermé).
+    """
+    hulls: dict[int, list[list[float]]] = {}
+    for j in range(k):
+        pts = coords_2d[labels == j]
+        if len(pts) < min_pts:
+            # Pas assez de points : bounding box
+            if len(pts) > 0:
+                mx_, my_ = pts.mean(axis=0)
+                r = max(0.01, pts.std(axis=0).max())
+                hulls[j] = [
+                    [mx_ - r, my_ - r], [mx_ + r, my_ - r],
+                    [mx_ + r, my_ + r], [mx_ - r, my_ + r],
+                ]
+            continue
+        try:
+            hull = ConvexHull(pts)
+            hull_pts = pts[hull.vertices].tolist()
+            # Fermer le polygone
+            hull_pts.append(hull_pts[0])
+            hulls[j] = hull_pts
+        except Exception:
+            hulls[j] = []
+    return hulls
+
+
+# ─── Nommage et scoring ───────────────────────────────────────────────────────
+
+def name_cluster(centroid: np.ndarray, raw_stats: dict) -> str:
+    """Nom lisible basé sur les features dominantes du centroïde [0,1]."""
+    s = centroid
+    n = len(s)
+    ttl_raw = float(raw_stats.get("mean_ttl", 0))
+    mss_raw = float(raw_stats.get("mean_mss", 0))
+    country_risk_v = s[20] if n > 20 else 0.0
+    asn_cloud      = s[21] if n > 21 else 0.0
+    accept_lang    = s[22] if n > 22 else 1.0
+    accept_enc     = s[23] if n > 23 else 1.0
+    sec_fetch      = s[24] if n > 24 else 0.0
+    hdr_count      = s[25] if n > 25 else 0.5
+    hfp_popular    = s[26] if n > 26 else 0.5
+    hfp_rotating   = s[27] if n > 27 else 0.0
+
+    # Scanner pur : aucun header browser, fingerprint rare, peu de headers
+    if accept_lang < 0.15 and accept_enc < 0.15 and hdr_count < 0.25:
+        return "🤖 Scanner pur (no headers)"
+    # Fingerprint tournant : bot qui change de profil headers
+    if hfp_rotating > 0.6:
+        return "🔄 Bot fingerprint tournant"
+    # Fingerprint très rare : bot artisanal unique
+    if hfp_popular < 0.15:
+        return "🕵️ Fingerprint rare suspect"
+    # Scanners Masscan
+    if s[0] > 0.16 and s[0] < 0.25 and mss_raw in range(1440, 1460) and s[2] > 0.25:
+        return "🤖 Masscan Scanner"
+    # Bots offensifs agressifs (fuzzing élevé)
+    if s[4] > 0.40 and s[5] > 0.3:
+        return "🤖 Bot agressif"
+    # Bot qui simule un navigateur mais sans les vrais headers
+    if s[15] > 0.40 and sec_fetch < 0.2 and accept_lang < 0.3:
+        return "🤖 Bot UA simulé"
+    # Pays à très haut risque avec infrastructure cloud
+    if country_risk_v > 0.75 and asn_cloud > 0.5:
+        return "🌏 Source pays risqué"
+    # Cloud + UA-CH mismatch
+    if s[15] > 0.50 and asn_cloud > 0.70:
+        return "☁️ Bot cloud UA-CH"
+    if s[15] > 0.60:
+        return "🤖 UA-CH Mismatch"
+    # Headless browser (Puppeteer/Playwright) : a les headers Sec-Fetch mais headless
+    if s[6] > 0.50 and sec_fetch > 0.5:
+        return "🤖 Headless Browser"
+    if s[6] > 0.50:
+        return "🤖 Headless (no Sec-Fetch)"
+    # Cloud pur (CDN/crawler légitime ?)
+    if asn_cloud > 0.85:
+        return "☁️ Infrastructure cloud"
+    # Pays à risque élevé sans autre signal
+    if country_risk_v > 0.60:
+        return "🌏 Trafic suspect (pays)"
+    # Navigateur légitime : tous les signaux positifs y compris fingerprint populaire
+    if (accept_lang > 0.7 and accept_enc > 0.7 and sec_fetch > 0.5
+            and hdr_count > 0.5 and hfp_popular > 0.5):
+        return "🌐 Navigateur légitime"
+    # OS fingerprinting
+    if s[3] > 0.85 and ttl_raw > 120:
+        return "🖥️ Windows"
+    if s[0] > 0.22 and s[0] < 0.28 and mss_raw > 1400:
+        return "🐧 Linux"
+    if mss_raw < 1380 and mss_raw > 0:
+        return "🌐 Tunnel réseau"
+    if s[4] > 0.40:
+        return "⚡ Trafic rapide"
+    if s[4] < 0.10 and asn_cloud < 0.30:
+        return "✅ Trafic sain"
+    return "📊 Cluster mixte"
+
+
+def risk_score_from_centroid(centroid: np.ndarray) -> float:
+    """
+    Score de risque [0,1] depuis le centroïde (espace original [0,1]).
+    30 features (avg_score supprimé) — poids calibrés pour sommer à 1.0.
+    Indices décalés de -1 après suppression de avg_score (ancien idx 4).
+    """
+    s = centroid
+    n = len(s)
+    country_risk_v = s[20] if n > 20 else 0.0
+    asn_cloud      = s[21] if n > 21 else 0.0
+    no_accept_lang = 1.0 - (s[22] if n > 22 else 1.0)
+    no_encoding    = 1.0 - (s[23] if n > 23 else 1.0)
+    no_sec_fetch   = 1.0 - (s[24] if n > 24 else 0.0)
+    few_headers    = 1.0 - (s[25] if n > 25 else 0.5)
+    hfp_rare       = 1.0 - (s[26] if n > 26 else 0.5)
+    hfp_rotating   = s[27] if n > 27 else 0.0
+
+    # [4]=vélocité  [5]=fuzzing  [6]=headless  [8]=ip_id_zero  [15]=ua_ch_mismatch
+    # Poids redistribués depuis l'ancien score ML anomalie (0.25) vers les signaux restants
+    return float(np.clip(
+        0.14 * s[5]          +   # fuzzing
+        0.17 * s[15]         +   # UA-CH mismatch (fort signal impersonation navigateur)
+        0.10 * s[6]          +   # headless
+        0.09 * s[4]          +   # vélocité (rps)
+        0.07 * s[8]          +   # IP-ID zéro
+        0.09 * country_risk_v+   # risque pays source
+        0.06 * asn_cloud     +   # infrastructure cloud/VPN
+        0.04 * no_accept_lang+   # absence Accept-Language
+        0.04 * no_encoding   +   # absence Accept-Encoding
+        0.04 * no_sec_fetch  +   # absence Sec-Fetch
+        0.04 * few_headers   +   # très peu de headers
+        0.06 * hfp_rare      +   # fingerprint rare = suspect
+        0.06 * hfp_rotating,     # rotation de fingerprint = bot
+        0.0, 1.0
+    ))
+
+
+# ─── Gradient de couleur basé sur le score de non-humanité ──────────────────
+# Le score [0,1] est mappé sur un dégradé HSL traversant tout le spectre :
+# bleu (humain) → cyan → vert → jaune-vert → jaune → orange → rouge (bot pur)
+# Hue : 220° (bleu froid) → 0° (rouge vif) en passant par tout l'arc chromatique.
+
+def _hsl_to_hex(h: float, s: float, l: float) -> str:
+    """Convertit HSL (h:0-360, s:0-100, l:0-100) en chaîne '#rrggbb'."""
+    s /= 100.0
+    l /= 100.0
+    c = (1.0 - abs(2.0 * l - 1.0)) * s
+    x = c * (1.0 - abs((h / 60.0) % 2.0 - 1.0))
+    m = l - c / 2.0
+    if   h < 60:  r, g, b = c, x, 0.0
+    elif h < 120: r, g, b = x, c, 0.0
+    elif h < 180: r, g, b = 0.0, c, x
+    elif h < 240: r, g, b = 0.0, x, c
+    elif h < 300: r, g, b = x, 0.0, c
+    else:         r, g, b = c, 0.0, x
+    ri, gi, bi = int((r + m) * 255), int((g + m) * 255), int((b + m) * 255)
+    return f"#{ri:02x}{gi:02x}{bi:02x}"
+
+
+def risk_to_gradient_color(risk: float) -> str:
+    """
+    Mappe un score de non-humanité [0,1] sur un dégradé HSL continu multi-stop.
+
+    risk = 0.0  → hue 220° (bleu froid   — trafic humain légitime)
+    risk = 0.25 → hue 165° (cyan-vert    — léger signal suspect)
+    risk = 0.50 → hue 110° (vert-jaune   — comportement mixte)
+    risk = 0.75 → hue  55° (jaune-orange — probable bot)
+    risk = 1.0  → hue   0° (rouge vif    — bot confirmé)
+
+    La saturation monte légèrement avec le risque pour accentuer la lisibilité.
+    """
+    r = float(np.clip(risk, 0.0, 1.0))
+    hue        = (1.0 - r) * 220.0          # 220° → 0°
+    saturation = 70.0 + r * 20.0            # 70% → 90%
+    lightness  = 58.0 - r * 10.0            # 58% → 48%  (plus sombre = plus alarmant)
+    return _hsl_to_hex(hue, saturation, lightness)

services/dashboard/backend/services/reputation_ip.py

@@ -0,0 +1,312 @@
+"""
+Services de réputation IP - Bases de données publiques sans clé API
+"""
+import httpx
+from typing import Optional, Dict, Any
+from datetime import datetime
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Timeout pour les requêtes HTTP
+HTTP_TIMEOUT = 10.0
+
+
+class IPReputationService:
+    """
+    Service de réputation IP utilisant des bases de données publiques gratuites
+    """
+    
+    def __init__(self):
+        self.http_client = httpx.AsyncClient(timeout=HTTP_TIMEOUT)
+        
+        # Sources de réputation (sans clé API)
+        self.sources = {
+            'ip_api': 'http://ip-api.com/json/{ip}',
+            'ipinfo': 'https://ipinfo.io/{ip}/json',
+        }
+    
+    async def get_reputation(self, ip: str) -> Dict[str, Any]:
+        """
+        Récupère la réputation d'une IP depuis toutes les sources disponibles
+        
+        Args:
+            ip: Adresse IP à vérifier
+            
+        Returns:
+            Dict avec les informations de réputation agrégées
+        """
+        results = {
+            'ip': ip,
+            'timestamp': datetime.utcnow().isoformat(),
+            'sources': {},
+            'aggregated': {
+                'is_proxy': False,
+                'is_hosting': False,
+                'is_vpn': False,
+                'is_tor': False,
+                'threat_score': 0,
+                'threat_level': 'unknown',
+                'country': None,
+                'asn': None,
+                'org': None,
+                'warnings': []
+            }
+        }
+        
+        # Interroge chaque source
+        for source_name, url_template in self.sources.items():
+            try:
+                url = url_template.format(ip=ip)
+                response = await self.http_client.get(url)
+                
+                if response.status_code == 200:
+                    data = response.json()
+                    results['sources'][source_name] = self._parse_source_data(source_name, data)
+                else:
+                    logger.warning(f"Source {source_name} returned status {response.status_code}")
+                    results['sources'][source_name] = {'error': f'Status {response.status_code}'}
+                    
+            except httpx.TimeoutException:
+                logger.warning(f"Timeout for source {source_name}")
+                results['sources'][source_name] = {'error': 'Timeout'}
+            except Exception as e:
+                logger.error(f"Error fetching from {source_name}: {str(e)}")
+                results['sources'][source_name] = {'error': str(e)}
+        
+        # Agrège les résultats
+        results['aggregated'] = self._aggregate_results(results['sources'])
+        
+        return results
+    
+    def _parse_source_data(self, source: str, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Parse les données d'une source spécifique
+        """
+        if source == 'ip_api':
+            return self._parse_ip_api(data)
+        elif source == 'ipinfo':
+            return self._parse_ipinfo(data)
+        return data
+    
+    def _parse_ip_api(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Parse les données de IP-API.com
+        
+        Response example:
+        {
+            "status": "success",
+            "country": "France",
+            "countryCode": "FR",
+            "region": "IDF",
+            "regionName": "Île-de-France",
+            "city": "Paris",
+            "zip": "75001",
+            "lat": 48.8534,
+            "lon": 2.3488,
+            "timezone": "Europe/Paris",
+            "isp": "OVH SAS",
+            "org": "OVH SAS",
+            "as": "AS16276 OVH SAS",
+            "asname": "OVH",
+            "mobile": false,
+            "proxy": false,
+            "hosting": true,
+            "query": "51.15.0.1"
+        }
+        """
+        if data.get('status') != 'success':
+            return {'error': data.get('message', 'Unknown error')}
+        
+        # Extraire l'ASN
+        asn_full = data.get('as', '')
+        asn_number = None
+        asn_org = None
+        
+        if asn_full:
+            parts = asn_full.split(' ', 1)
+            if len(parts) >= 1:
+                asn_number = parts[0].replace('AS', '')
+            if len(parts) >= 2:
+                asn_org = parts[1]
+        
+        return {
+            'country': data.get('country'),
+            'country_code': data.get('countryCode'),
+            'region': data.get('regionName'),
+            'city': data.get('city'),
+            'isp': data.get('isp'),
+            'org': data.get('org'),
+            'asn': asn_number,
+            'asn_org': asn_org,
+            'is_proxy': data.get('proxy', False),
+            'is_hosting': data.get('hosting', False),
+            'is_mobile': data.get('mobile', False),
+            'timezone': data.get('timezone'),
+            'lat': data.get('lat'),
+            'lon': data.get('lon'),
+            'query': data.get('query')
+        }
+    
+    def _parse_ipinfo(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Parse les données de IPinfo.io
+        
+        Response example:
+        {
+            "ip": "51.15.0.1",
+            "city": "Paris",
+            "region": "Île-de-France",
+            "country": "FR",
+            "loc": "48.8534,2.3488",
+            "org": "AS16276 OVH SAS",
+            "postal": "75001",
+            "timezone": "Europe/Paris",
+            "readme": "https://ipinfo.io/missingauth"
+        }
+        """
+        # Extraire l'ASN
+        org_full = data.get('org', '')
+        asn_number = None
+        asn_org = None
+        
+        if org_full:
+            parts = org_full.split(' ', 1)
+            if len(parts) >= 1:
+                asn_number = parts[0].replace('AS', '')
+            if len(parts) >= 2:
+                asn_org = parts[1]
+        
+        # Extraire lat/lon
+        loc = data.get('loc', '')
+        lat = None
+        lon = None
+        if loc:
+            coords = loc.split(',')
+            if len(coords) == 2:
+                lat = float(coords[0])
+                lon = float(coords[1])
+        
+        return {
+            'ip': data.get('ip'),
+            'city': data.get('city'),
+            'region': data.get('region'),
+            'country': data.get('country'),
+            'postal': data.get('postal'),
+            'timezone': data.get('timezone'),
+            'asn': asn_number,
+            'asn_org': asn_org,
+            'org': data.get('org'),
+            'lat': lat,
+            'lon': lon
+        }
+    
+    def _aggregate_results(self, sources: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        Agrège les résultats de toutes les sources
+        
+        Logique d'agrégation:
+        - is_proxy: true si au moins une source le détecte
+        - is_hosting: true si au moins une source le détecte
+        - threat_score: basé sur les détections proxy/hosting/vpn/tor
+        - threat_level: low/medium/high/critical basé sur le score
+        """
+        aggregated = {
+            'is_proxy': False,
+            'is_hosting': False,
+            'is_vpn': False,
+            'is_tor': False,
+            'threat_score': 0,
+            'threat_level': 'unknown',
+            'country': None,
+            'country_code': None,
+            'asn': None,
+            'asn_org': None,
+            'org': None,
+            'city': None,
+            'warnings': []
+        }
+        
+        threat_score = 0
+        
+        for source_name, source_data in sources.items():
+            if 'error' in source_data:
+                continue
+            
+            # Proxy detection
+            if source_data.get('is_proxy'):
+                aggregated['is_proxy'] = True
+                threat_score += 30
+                aggregated['warnings'].append(f'{source_name}: Proxy détecté')
+            
+            # Hosting detection
+            if source_data.get('is_hosting'):
+                aggregated['is_hosting'] = True
+                threat_score += 20
+                aggregated['warnings'].append(f'{source_name}: Hébergement cloud/datacenter')
+            
+            # VPN detection (si disponible)
+            if source_data.get('is_vpn'):
+                aggregated['is_vpn'] = True
+                threat_score += 40
+                aggregated['warnings'].append(f'{source_name}: VPN détecté')
+            
+            # Tor detection (si disponible)
+            if source_data.get('is_tor'):
+                aggregated['is_tor'] = True
+                threat_score += 50
+                aggregated['warnings'].append(f'{source_name}: Exit node Tor détecté')
+            
+            # Infos géographiques (prend la première disponible)
+            if not aggregated['country'] and source_data.get('country'):
+                aggregated['country'] = source_data.get('country')
+            
+            if not aggregated['country_code'] and source_data.get('country_code'):
+                aggregated['country_code'] = source_data.get('country_code')
+            
+            # ASN (prend la première disponible)
+            if not aggregated['asn'] and source_data.get('asn'):
+                aggregated['asn'] = source_data.get('asn')
+            
+            if not aggregated['asn_org'] and source_data.get('asn_org'):
+                aggregated['asn_org'] = source_data.get('asn_org')
+            
+            # Organisation/ISP
+            if not aggregated['org'] and source_data.get('org'):
+                aggregated['org'] = source_data.get('org')
+            
+            # Ville
+            if not aggregated['city'] and source_data.get('city'):
+                aggregated['city'] = source_data.get('city')
+        
+        # Calcul du niveau de menace
+        aggregated['threat_score'] = min(100, threat_score)
+        
+        if threat_score >= 80:
+            aggregated['threat_level'] = 'critical'
+        elif threat_score >= 60:
+            aggregated['threat_level'] = 'high'
+        elif threat_score >= 40:
+            aggregated['threat_level'] = 'medium'
+        elif threat_score >= 20:
+            aggregated['threat_level'] = 'low'
+        else:
+            aggregated['threat_level'] = 'clean'
+        
+        return aggregated
+    
+    async def close(self):
+        """Ferme le client HTTP"""
+        await self.http_client.aclose()
+
+
+# Singleton pour réutiliser le service
+_reputation_service: Optional[IPReputationService] = None
+
+
+def get_reputation_service() -> IPReputationService:
+    """Retourne l'instance singleton du service de réputation"""
+    global _reputation_service
+    if _reputation_service is None:
+        _reputation_service = IPReputationService()
+    return _reputation_service

services/dashboard/backend/services/tcp_fingerprint.py

@@ -0,0 +1,436 @@
+"""
+Service de fingerprinting OS par signature TCP — approche multi-signal inspirée de p0f.
+
+Signaux utilisés (par ordre de poids) :
+  1. TTL initial estimé  (→ famille OS : Linux/Mac=64, Windows=128, Cisco/BSD=255)
+  2. MSS                 (→ type de réseau : Ethernet=1460, PPPoE=1452, VPN=1380-1420)
+  3. Taille de fenêtre   (→ signature OS précise)
+  4. Facteur d'échelle   (→ affine la version du kernel/stack TCP)
+
+Références :
+  - p0f v3 (Michal Zalewski) — passive OS fingerprinting
+  - Nmap OS detection (Gordon Lyon)
+  - "OS Fingerprinting Revisited" (Beverly, 2004)
+  - "Passive OS fingerprinting" (Orebaugh, Ramirez)
+  - Recherche sur Masscan/ZMap : signatures SYN craftées connues
+"""
+
+from __future__ import annotations
+from dataclasses import dataclass
+from typing import Optional
+
+
+# ─── Constantes ───────────────────────────────────────────────────────────────
+
+_INITIAL_TTLS = (64, 128, 255)
+
+# MSS → type de chemin réseau (MTU - 40 octets d'en-têtes IP+TCP)
+_MSS_PATH: list[tuple[range, str]] = [
+    (range(1461, 9001), "Ethernet/Jumbo"),   # jumbo frames (CDN/datacenter)
+    (range(1460, 1461), "Ethernet directe"),  # MTU 1500 standard
+    (range(1453, 1460), "Ethernet directe"),  # légèrement réduit (padding)
+    (range(1452, 1453), "PPPoE/DSL"),         # MTU 1492
+    (range(1436, 1452), "PPPoE/DSL ajusté"), # variations DSL
+    (range(1420, 1436), "VPN léger"),         # WireGuard / IPsec transport
+    (range(1380, 1420), "VPN/Tunnel"),        # OpenVPN / L2TP
+    (range(1300, 1380), "VPN double ou mobile"),
+    (range(0,    1300), "Lien bas débit / GPRS"),
+]
+
+
+# ─── Base de signatures OS ─────────────────────────────────────────────────────
+#
+# Format : chaque entrée est un dict avec :
+#   ttl   : int            — TTL initial attendu (64 | 128 | 255)
+#   win   : set[int]|None  — tailles de fenêtre attendues (None = ignorer)
+#   scale : set[int]|None  — facteurs d'échelle attendus  (None = ignorer)
+#   mss   : set[int]|None  — valeurs MSS attendues        (None = ignorer)
+#   name  : str            — libellé affiché
+#   conf  : float          — poids de confiance de base (0–1)
+#   bot   : bool           — outil de scan/bot connu
+
+_SIGNATURES: list[dict] = [
+    # ══════════════════════════════════════════════════════
+    # OUTILS DE SCAN ET BOTS CONNUS (priorité maximale)
+    # ══════════════════════════════════════════════════════
+
+    # Masscan / scanner personnalisé avec stack Linux modifiée (PPPoE MSS=1452)
+    # Pattern très présent dans les données : ~111k requêtes, UA spoofé macOS/Windows
+    {
+        "ttl": 64, "win": {5808}, "scale": {4}, "mss": {1452},
+        "name": "Bot-Scanner/Masscan", "conf": 0.97, "bot": True,
+    },
+    # Masscan TTL=255 (mode direct, pas de hop)
+    {
+        "ttl": 255, "win": {1024}, "scale": {0}, "mss": None,
+        "name": "Bot-ZMap/Masscan", "conf": 0.96, "bot": True,
+    },
+    # Mirai variant (petite fenêtre, pas de scale, TTL Linux)
+    {
+        "ttl": 64, "win": {1024, 2048}, "scale": {0}, "mss": {1460},
+        "name": "Bot-Mirai", "conf": 0.92, "bot": True,
+    },
+    # Mirai variant (petite fenêtre Windows)
+    {
+        "ttl": 128, "win": {1024, 2048}, "scale": {0}, "mss": {1460},
+        "name": "Bot-Mirai/Win", "conf": 0.92, "bot": True,
+    },
+    # Scapy / forge manuelle (fenêtre 8192 exactement + TTL 64 + pas de scale)
+    {
+        "ttl": 64, "win": {8192}, "scale": {0}, "mss": {1460},
+        "name": "Bot-Scapy/Forge", "conf": 0.85, "bot": True,
+    },
+    # Nmap SYN scan (window=1024, MSS=1460, TTL=64 ou 128)
+    {
+        "ttl": 64, "win": {1}, "scale": None, "mss": None,
+        "name": "Bot-ZMap", "conf": 0.95, "bot": True,
+    },
+
+    # ══════════════════════════════════════════════════════
+    # WINDOWS
+    # ══════════════════════════════════════════════════════
+
+    # Windows 10 / 11 — signature standard (LAN direct)
+    {
+        "ttl": 128, "win": {64240}, "scale": {8}, "mss": {1460},
+        "name": "Windows 10/11", "conf": 0.93, "bot": False,
+    },
+    # Windows 10/11 — derrière VPN/proxy (MSS réduit)
+    {
+        "ttl": 128, "win": {64240}, "scale": {8}, "mss": {1380, 1400, 1412, 1420, 1440},
+        "name": "Windows 10/11 (VPN)", "conf": 0.90, "bot": False,
+    },
+    # Windows Server 2019/2022 — grande fenêtre
+    {
+        "ttl": 128, "win": {65535, 131072}, "scale": {8, 9}, "mss": {1460},
+        "name": "Windows Server", "conf": 0.88, "bot": False,
+    },
+    # Windows 7/8.1
+    {
+        "ttl": 128, "win": {8192, 65535}, "scale": {4, 8}, "mss": {1460},
+        "name": "Windows 7/8", "conf": 0.83, "bot": False,
+    },
+    # Windows générique (TTL=128, scale=8, tout MSS)
+    {
+        "ttl": 128, "win": None, "scale": {8}, "mss": None,
+        "name": "Windows", "conf": 0.70, "bot": False,
+    },
+
+    # ══════════════════════════════════════════════════════
+    # ANDROID (stack BBRv2 / CUBIC moderne)
+    # ══════════════════════════════════════════════════════
+
+    # Android 10+ — scale=9 ou 10, grande fenêtre (BBRv2)
+    {
+        "ttl": 64, "win": {65535, 131072, 42340, 35844}, "scale": {9, 10}, "mss": {1460},
+        "name": "Android 10+", "conf": 0.82, "bot": False,
+    },
+    # Android via proxy TTL=128 (app Facebook, TikTok etc. passant par infra)
+    {
+        "ttl": 128, "win": {62727, 65535}, "scale": {7}, "mss": {1460},
+        "name": "Android/App (proxy)", "conf": 0.75, "bot": False,
+    },
+    # Android derrière VPN (MSS réduit)
+    {
+        "ttl": 64, "win": {65535, 59640, 63940}, "scale": {8, 9, 10}, "mss": {1380, 1390, 1400, 1418, 1420},
+        "name": "Android (VPN/mobile)", "conf": 0.78, "bot": False,
+    },
+
+    # ══════════════════════════════════════════════════════
+    # iOS / macOS
+    # ══════════════════════════════════════════════════════
+
+    # iOS 14+ / macOS Monterey+ — scale=6, win=65535 (signature XNU)
+    {
+        "ttl": 64, "win": {65535, 32768}, "scale": {6}, "mss": {1460},
+        "name": "iOS/macOS", "conf": 0.87, "bot": False,
+    },
+    # macOS Sonoma+ / iOS 17+ (scale=9, fenêtre plus grande)
+    {
+        "ttl": 64, "win": {65535, 32768}, "scale": {9}, "mss": {1460},
+        "name": "macOS Sonoma+/iOS 17+", "conf": 0.83, "bot": False,
+    },
+    # macOS derrière VPN (MSS réduit)
+    {
+        "ttl": 64, "win": {65535}, "scale": {6, 9}, "mss": {1380, 1400, 1412, 1436},
+        "name": "iOS/macOS (VPN)", "conf": 0.80, "bot": False,
+    },
+
+    # ══════════════════════════════════════════════════════
+    # LINUX (desktop/serveur)
+    # ══════════════════════════════════════════════════════
+
+    # Linux 5.x+ — scale=7, win=64240 ou 65535 (kernel ≥ 4.19)
+    {
+        "ttl": 64, "win": {64240, 65320}, "scale": {7}, "mss": {1460},
+        "name": "Linux 5.x+", "conf": 0.86, "bot": False,
+    },
+    # Linux 4.x / ChromeOS
+    {
+        "ttl": 64, "win": {29200, 65535, 43690, 32120}, "scale": {7}, "mss": {1460},
+        "name": "Linux 4.x/ChromeOS", "conf": 0.83, "bot": False,
+    },
+    # Linux derrière VPN (MSS réduit)
+    {
+        "ttl": 64, "win": {64240, 65535, 42600}, "scale": {7}, "mss": {1380, 1400, 1420, 1436},
+        "name": "Linux (VPN)", "conf": 0.80, "bot": False,
+    },
+    # Linux 2.6.x (ancien — win=5840/14600)
+    {
+        "ttl": 64, "win": {5840, 14600, 16384}, "scale": {4, 5}, "mss": {1460},
+        "name": "Linux 2.6", "conf": 0.78, "bot": False,
+    },
+
+    # ══════════════════════════════════════════════════════
+    # BSD / ÉQUIPEMENTS RÉSEAU / CDN
+    # ══════════════════════════════════════════════════════
+
+    # FreeBSD / OpenBSD (initial TTL=64)
+    {
+        "ttl": 64, "win": {65535}, "scale": {6}, "mss": {512, 1460},
+        "name": "FreeBSD/OpenBSD", "conf": 0.74, "bot": False,
+    },
+    # Cisco IOS / équipements réseau (initial TTL=255, fenêtre petite)
+    {
+        "ttl": 255, "win": {4096, 4128, 8760}, "scale": {0, 1, 2}, "mss": {512, 1460},
+        "name": "Cisco/Réseau", "conf": 0.87, "bot": False,
+    },
+    # CDN / Applebot (TTL=255, jumbo MSS, fenêtre élevée)
+    {
+        "ttl": 255, "win": {26883, 65535, 59640}, "scale": {7, 8}, "mss": {8373, 8365, 1460},
+        "name": "CDN/Applebot (jumbo)", "conf": 0.85, "bot": False,
+    },
+    # BSD/Unix générique (TTL=255)
+    {
+        "ttl": 255, "win": None, "scale": {6, 7, 8}, "mss": {1460},
+        "name": "BSD/Unix", "conf": 0.68, "bot": False,
+    },
+]
+
+
+# ─── Data classes ──────────────────────────────────────────────────────────────
+
+@dataclass
+class OsFingerprint:
+    os_name:      str
+    initial_ttl:  int
+    hop_count:    int
+    confidence:   float
+    is_bot_tool:  bool
+    network_path: str
+
+
+@dataclass
+class SpoofResult:
+    is_spoof:    bool
+    is_bot_tool: bool
+    reason:      str
+
+
+# ─── Fonctions utilitaires ─────────────────────────────────────────────────────
+
+def _estimate_initial_ttl(observed_ttl: int) -> tuple[int, int]:
+    """Retourne (initial_ttl, hop_count).
+    Cherche le TTL standard le plus bas >= observed_ttl.
+    Rejette les hop counts > 45 (réseau légitimement long = peu probable).
+    """
+    if observed_ttl <= 0:
+        return 0, -1
+    for initial in _INITIAL_TTLS:
+        if observed_ttl <= initial:
+            hop = initial - observed_ttl
+            if hop <= 45:
+                return initial, hop
+    return 255, 255 - observed_ttl  # TTL > 255 impossible, fallback
+
+
+def _infer_network_path(mss: int) -> str:
+    """Retourne le type de chemin réseau probable à partir du MSS."""
+    if mss <= 0:
+        return "Inconnu"
+    for rng, label in _MSS_PATH:
+        if mss in rng:
+            return label
+    return "Inconnu"
+
+
+def _os_family(os_name: str) -> str:
+    """Réduit un nom OS détaillé à sa famille pour comparaison avec l'UA."""
+    n = os_name.lower()
+    if "windows" in n:
+        return "Windows"
+    if "android" in n:
+        return "Android"
+    if "ios" in n or "macos" in n or "iphone" in n or "ipad" in n:
+        return "Apple"
+    if "linux" in n or "chromeos" in n:
+        return "Linux"
+    if "bsd" in n or "cisco" in n or "cdn" in n or "réseau" in n:
+        return "Network"
+    if "bot" in n or "scanner" in n or "mirai" in n or "zmap" in n:
+        return "Bot"
+    return "Unknown"
+
+
+def _ua_os_family(declared_os: str) -> str:
+    """Réduit l'OS déclaré (UA) à sa famille."""
+    mapping = {
+        "Windows":      "Windows",
+        "Android":      "Android",
+        "iOS":          "Apple",
+        "macOS":        "Apple",
+        "Linux":        "Linux",
+        "ChromeOS":     "Linux",
+        "BSD":          "Network",
+    }
+    return mapping.get(declared_os, "Unknown")
+
+
+# ─── Fonctions publiques ───────────────────────────────────────────────────────
+
+def declared_os_from_ua(ua: str) -> str:
+    """Infère l'OS déclaré à partir du User-Agent."""
+    ua = ua or ""
+    ul = ua.lower()
+    if not ul:
+        return "Unknown"
+    if "windows nt" in ul:
+        return "Windows"
+    if "android" in ul:
+        return "Android"
+    if "iphone" in ul or "ipad" in ul or "cpu iphone" in ul or "cpu ipad" in ul:
+        return "iOS"
+    if "mac os x" in ul or "macos" in ul:
+        return "macOS"
+    if "cros" in ul or "chromeos" in ul:
+        return "ChromeOS"
+    if "linux" in ul:
+        return "Linux"
+    if "freebsd" in ul or "openbsd" in ul or "netbsd" in ul:
+        return "BSD"
+    return "Unknown"
+
+
+def fingerprint_os(ttl: int, win: int, scale: int, mss: int) -> OsFingerprint:
+    """Fingerprint OS multi-signal avec scoring pondéré.
+
+    Poids des signaux :
+      - TTL initial  40 % (discriminant principal : famille OS)
+      - MSS          30 % (type de réseau ET OS)
+      - Fenêtre TCP  20 % (version/distrib précise)
+      - Scale        10 % (affine la version kernel)
+    """
+    initial_ttl, hop_count = _estimate_initial_ttl(ttl)
+    network_path = _infer_network_path(mss)
+
+    if initial_ttl == 0:
+        return OsFingerprint(
+            os_name="Unknown", initial_ttl=0, hop_count=-1,
+            confidence=0.0, is_bot_tool=False, network_path=network_path,
+        )
+
+    best_score: float = -1.0
+    best_sig: Optional[dict] = None
+
+    for sig in _SIGNATURES:
+        # Le TTL est un filtre strict — pas de correspondance, on passe
+        if sig["ttl"] != initial_ttl:
+            continue
+
+        score: float = 0.40  # Score de base pour correspondance TTL
+
+        # MSS (poids 0.30)
+        if sig["mss"] is not None:
+            score += 0.30 if mss in sig["mss"] else -0.12
+
+        # Fenêtre (poids 0.20)
+        if sig["win"] is not None:
+            score += 0.20 if win in sig["win"] else -0.08
+
+        # Scale (poids 0.10)
+        if sig["scale"] is not None:
+            score += 0.10 if scale in sig["scale"] else -0.04
+
+        # Pénalité si hop count anormalement élevé (>30 hops)
+        if hop_count > 30:
+            score -= 0.05
+
+        if score > best_score:
+            best_score = score
+            best_sig = sig
+
+    if best_sig and best_score >= 0.38:
+        # Pondérer la confiance finale par le score et le conf de la signature
+        raw_conf = best_score * best_sig["conf"]
+        confidence = round(min(max(raw_conf, 0.0), 1.0), 2)
+        return OsFingerprint(
+            os_name=best_sig["name"],
+            initial_ttl=initial_ttl,
+            hop_count=hop_count,
+            confidence=confidence,
+            is_bot_tool=best_sig["bot"],
+            network_path=network_path,
+        )
+
+    # Repli : classification TTL seule (confiance minimale)
+    fallback = {64: "Linux/macOS", 128: "Windows", 255: "Cisco/BSD"}
+    return OsFingerprint(
+        os_name=fallback.get(initial_ttl, "Unknown"),
+        initial_ttl=initial_ttl,
+        hop_count=hop_count,
+        confidence=round(0.40 * 0.65, 2),  # confiance faible
+        is_bot_tool=False,
+        network_path=network_path,
+    )
+
+
+def detect_spoof(fp: OsFingerprint, declared_os: str) -> SpoofResult:
+    """Détecte les incohérences OS entre TCP et UA.
+
+    Règles :
+      1. Outil de scan connu → spoof/bot, quelle que soit l'UA
+      2. Confiance < 0.50 → indéterminable
+      3. OS incompatibles → spoof confirmé
+      4. Cohérent → OK
+    """
+    if fp.is_bot_tool:
+        return SpoofResult(
+            is_spoof=True,
+            is_bot_tool=True,
+            reason=f"Outil de scan détecté ({fp.os_name})",
+        )
+
+    if fp.confidence < 0.50 or fp.os_name == "Unknown" or declared_os == "Unknown":
+        return SpoofResult(
+            is_spoof=False,
+            is_bot_tool=False,
+            reason="Corrélation insuffisante",
+        )
+
+    tcp_family = _os_family(fp.os_name)
+    ua_family  = _ua_os_family(declared_os)
+
+    # Les familles Network/Bot sont toujours suspectes si l'UA prétend être un navigateur
+    if tcp_family == "Network" and ua_family not in ("Network", "Unknown"):
+        return SpoofResult(
+            is_spoof=True,
+            is_bot_tool=False,
+            reason=f"Équipement réseau/CDN (TCP) vs {declared_os} (UA)",
+        )
+
+    if tcp_family == "Unknown" or ua_family == "Unknown":
+        return SpoofResult(is_spoof=False, is_bot_tool=False, reason="OS indéterminé")
+
+    # Android passant par un proxy infra (ex: Facebook app → proxy Windows)
+    # → pas forcément un spoof, noté mais non flaggé
+    if declared_os == "Android" and tcp_family == "Windows" and "proxy" in fp.os_name.lower():
+        return SpoofResult(is_spoof=False, is_bot_tool=False, reason="App mobile via proxy infra")
+
+    if tcp_family != ua_family:
+        return SpoofResult(
+            is_spoof=True,
+            is_bot_tool=False,
+            reason=f"TCP→{tcp_family} vs UA→{ua_family}",
+        )
+
+    return SpoofResult(is_spoof=False, is_bot_tool=False, reason="Cohérent")

services/dashboard/backend/tests/conftest.py

@@ -0,0 +1,18 @@
+import pytest
+from unittest.mock import MagicMock, patch
+from fastapi.testclient import TestClient
+
+
+@pytest.fixture
+def mock_db():
+    db = MagicMock()
+    db.query.return_value = MagicMock(result_rows=[])
+    return db
+
+
+@pytest.fixture
+def client(mock_db):
+    with patch("backend.database.db", mock_db):
+        from backend.main import app
+        with TestClient(app) as c:
+            yield c, mock_db

services/dashboard/backend/tests/test_audit.py

@@ -0,0 +1,10 @@
+def test_audit_log_post(client):
+    c, _ = client
+    resp = c.post("/api/audit/logs?action=test_action&user=testuser")
+    assert resp.status_code in (200, 422, 404)
+
+
+def test_audit_log_get(client):
+    c, _ = client
+    resp = c.get("/api/audit/logs?hours=1")
+    assert resp.status_code in (200, 404)

services/dashboard/backend/tests/test_detections.py

@@ -0,0 +1,70 @@
+"""Tests for the detections routes and helper functions."""
+import pytest
+
+
+def test_detections_list_endpoint(client):
+    """GET /api/detections returns a valid status code."""
+    c, mock_db = client
+    mock_db.query.return_value.result_rows = [(50,)]  # count query
+    resp = c.get("/api/detections")
+    assert resp.status_code in (200, 404, 422, 500)
+
+
+def test_detections_list_with_filters(client):
+    """GET /api/detections supports filter query params."""
+    c, mock_db = client
+    mock_db.query.return_value.result_rows = [(0,)]
+    resp = c.get("/api/detections?threat_level=CRITICAL&page=1&page_size=10")
+    assert resp.status_code in (200, 404, 422, 500)
+
+
+def test_detections_pagination(client):
+    """GET /api/detections supports pagination params."""
+    c, mock_db = client
+    mock_db.query.return_value.result_rows = [(0,)]
+    resp = c.get("/api/detections?page=2&page_size=10")
+    assert resp.status_code in (200, 404, 422, 500)
+
+
+def test_label_to_score_known_labels():
+    """_label_to_score returns known float values for recognized labels."""
+    from backend.routes.detections import _label_to_score
+    assert _label_to_score("human") == pytest.approx(0.9)
+    assert _label_to_score("bot") == pytest.approx(0.05)
+    assert _label_to_score("tor") == pytest.approx(0.1)
+    assert _label_to_score("proxy") == pytest.approx(0.25)
+
+
+def test_label_to_score_unknown_label():
+    """_label_to_score returns 0.5 for unrecognized labels."""
+    from backend.routes.detections import _label_to_score
+    assert _label_to_score("unknown_label") == pytest.approx(0.5)
+
+
+def test_label_to_score_empty_string():
+    """_label_to_score returns None for empty string."""
+    from backend.routes.detections import _label_to_score
+    assert _label_to_score("") is None
+
+
+def test_label_to_score_case_insensitive():
+    """_label_to_score is case-insensitive."""
+    from backend.routes.detections import _label_to_score
+    assert _label_to_score("HUMAN") == _label_to_score("human")
+    assert _label_to_score("Bot") == _label_to_score("bot")
+
+
+def test_detections_search_filter(client):
+    """GET /api/detections supports search text filter."""
+    c, mock_db = client
+    mock_db.query.return_value.result_rows = [(0,)]
+    resp = c.get("/api/detections?search=1.2.3")
+    assert resp.status_code in (200, 404, 422, 500)
+
+
+def test_detections_group_by_ip(client):
+    """GET /api/detections supports group_by_ip mode."""
+    c, mock_db = client
+    mock_db.query.return_value.result_rows = [(0,)]
+    resp = c.get("/api/detections?group_by_ip=true")
+    assert resp.status_code in (200, 404, 422, 500)

services/dashboard/backend/tests/test_health.py

@@ -0,0 +1,26 @@
+def test_health_returns_200(client):
+    c, _ = client
+    resp = c.get("/health")
+    assert resp.status_code == 200
+
+
+def test_health_endpoint_body(client):
+    """Health endpoint returns a body with 'status'."""
+    c, _ = client
+    resp = c.get("/health")
+    assert resp.status_code == 200
+    # Body may be JSON or plain text
+    try:
+        data = resp.json()
+        assert "status" in data
+    except Exception:
+        pass  # Non-JSON health check body is also acceptable
+
+
+def test_health_db_not_required(client):
+    """Health check does not depend on DB availability."""
+    c, mock_db = client
+    mock_db.query.side_effect = Exception("DB down")
+    resp = c.get("/health")
+    # Health should still return 200 even if DB throws
+    assert resp.status_code == 200

services/dashboard/backend/tests/test_metrics.py

@@ -0,0 +1,34 @@
+def test_metrics_endpoint(client):
+    c, mock_db = client
+    mock_db.query.return_value.result_rows = [
+        ("1.2.3.4", "t1234567890abc", "UA/5.0", "FR", 100)
+    ]
+    resp = c.get("/api/metrics/top-ips?hours=1&limit=10")
+    assert resp.status_code in (200, 404, 422)  # endpoint may not exist in all versions
+
+
+def test_metrics_main_endpoint(client):
+    """GET /api/metrics returns 200 when DB returns data."""
+    c, mock_db = client
+    # Summary row: total, critical, high, medium, low, known_bots, anomalies, unique_ips
+    mock_db.query.return_value.result_rows = [
+        (100, 5, 10, 20, 65, 15, 85, 50)
+    ]
+    resp = c.get("/api/metrics")
+    assert resp.status_code in (200, 404, 422, 500)
+
+
+def test_metrics_main_no_data(client):
+    """GET /api/metrics returns 404 when DB returns no rows."""
+    c, mock_db = client
+    mock_db.query.return_value.result_rows = []
+    resp = c.get("/api/metrics")
+    assert resp.status_code in (404, 500)
+
+
+def test_threats_endpoint(client):
+    """GET /api/metrics/threats returns acceptable status code."""
+    c, mock_db = client
+    mock_db.query.return_value.result_rows = [("CRITICAL", 5), ("HIGH", 10)]
+    resp = c.get("/api/metrics/threats")
+    assert resp.status_code in (200, 404, 422, 500)

services/dashboard/backend/tests/test_reputation.py

@@ -0,0 +1,25 @@
+import pytest
+
+PRIVATE_RANGES = [
+    "127.0.0.1", "10.0.0.1", "192.168.1.1", "172.16.0.1",
+    "169.254.0.1", "::1", "fc00::1"
+]
+
+
+def is_private_ip(ip: str) -> bool:
+    import ipaddress
+    try:
+        addr = ipaddress.ip_address(ip)
+        return addr.is_private or addr.is_loopback or addr.is_link_local
+    except ValueError:
+        return True
+
+
+def test_private_ips_rejected():
+    for ip in PRIVATE_RANGES:
+        assert is_private_ip(ip), f"{ip} should be private"
+
+
+def test_public_ip_accepted():
+    assert not is_private_ip("8.8.8.8")
+    assert not is_private_ip("1.1.1.1")