Files
dashboard/backend/models.py
SOC Analyst d4c3512572 feat: 6 améliorations SOC — synthèse IP, baseline, sophistication, chasse proactive, badge ASN, 2 nouveaux onglets rotation
- investigation_summary.py: nouveau endpoint GET /api/investigation/{ip}/summary
  agrège 6 sources (ML, bruteforce, TCP spoofing, JA4 rotation, persistance, timeline 24h)
  en un score de risque 0-100 avec signaux détaillés
- InvestigationView.tsx: widget IPActivitySummary avec jauge Risk Score SVG,
  badges multi-sources et mini-timeline 24h barres
- metrics.py: endpoint GET /api/metrics/baseline — comparaison 24h vs hier
  (total détections, IPs uniques, alertes CRITICAL) avec % de variation
- IncidentsView.tsx: widget baseline avec ▲▼ sur le dashboard principal
- rotation.py: endpoints /sophistication et /proactive-hunt
  Score sophistication = JOIN 3 tables (rotation×10 + récurrence×20 + log(bf+1)×5)
  Chasse proactive = IPs récurrentes sous le seuil ML (abs(score) < 0.5)
- RotationView.tsx: onglets 🏆 Sophistication et 🕵️ Chasse proactive
  avec tier APT-like/Advanced/Automated/Basic et boutons investigation
- detections.py: LEFT JOIN asn_reputation, badge coloré rouge/orange/vert
  selon label (bot/scanner → score 0.05, human → 0.9)
- models.py: ajout champs asn_score et asn_rep_label dans Detection

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-03-16 00:43:27 +01:00

358 lines
12 KiB
Python

"""
Modèles de données pour l'API
"""
from pydantic import BaseModel, Field
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"
class ModelName(str, Enum):
COMPLET = "Complet"
APPLICATIF = "Applicatif"
# ─────────────────────────────────────────────────────────────────────────────
# 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 = ""
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
# ─────────────────────────────────────────────────────────────────────────────
# COMPARAISON
# ─────────────────────────────────────────────────────────────────────────────
class ComparisonMetric(BaseModel):
name: str
value1: Any
value2: Any
difference: str
trend: str # "better", "worse", "same"
class ComparisonEntity(BaseModel):
type: str
value: str
total_detections: int
unique_ips: int
avg_score: float
primary_threat: str
class ComparisonResponse(BaseModel):
entity1: ComparisonEntity
entity2: ComparisonEntity
metrics: List[ComparisonMetric]
# ─────────────────────────────────────────────────────────────────────────────
# 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"""
created_at: datetime
features: dict = Field(default_factory=dict)
class Config:
from_attributes = True
class ClassificationStats(BaseModel):
"""Statistiques de classification"""
label: str
total: int
unique_ips: int
avg_confidence: float
class ClassificationsListResponse(BaseModel):
"""Réponse pour la liste des classifications"""
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)