feat: clustering multi-métriques + TCP fingerprinting amélioré
- TCP fingerprinting: 20 signatures OS (p0f-style), scoring multi-signal
TTL/MSS/scale/fenêtre, détection Masscan 97% confiance, réseau path
(Ethernet/PPPoE/VPN/Tunnel), estimation hop-count
- Clustering IPs: K-means++ (Arthur & Vassilvitskii 2007) sur 21 features
TCP stack + anomalie ML + TLS/protocole + navigateur + temporel
PCA-2D par puissance itérative (Hotelling) pour positionnement
- Visualisation redesign: 2 vues lisibles
- Tableau de bord: grille de cartes groupées par niveau de risque
(Bots / Suspects / Légitimes), métriques clés + mini-barres
- Graphe de relations: ReactFlow avec nœuds-cartes en colonnes
par niveau de menace, arêtes colorées par similarité, légende
- Sidebar: RadarChart comportemental + toutes métriques + export CSV
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
SOC Analyst
458
backend/routes/clustering.py
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458
backend/routes/clustering.py
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@ -0,0 +1,458 @@
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"""
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Clustering d'IPs multi-métriques — backend ReactFlow.
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Features utilisées (21 dimensions) :
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TCP stack : TTL initial, MSS, scale, fenêtre TCP
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Comportement : vélocité, POST ratio, fuzzing, assets, accès direct
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Anomalie ML : score, IP-ID zéro
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TLS/Protocole: ALPN mismatch, ALPN absent, efficacité H2
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Navigateur : browser score, headless, ordre headers, UA-CH mismatch
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Temporel : entropie, diversité JA4, UA rotatif
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Algorithme :
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1. Échantillonnage stratifié (top détections + top hits)
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2. Construction + normalisation des vecteurs de features
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3. K-means++ (Arthur & Vassilvitskii, 2007)
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4. PCA-2D par power iteration pour les positions ReactFlow
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5. Nommage automatique par features dominantes du centroïde
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6. Calcul des arêtes : k-NN dans l'espace des features
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"""
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from __future__ import annotations
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import math
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import time
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import hashlib
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from typing import Optional
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from fastapi import APIRouter, HTTPException, Query
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from ..database import db
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from ..services.clustering_engine import (
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FEATURES, FEATURE_KEYS, FEATURE_NORMS, FEATURE_NAMES, N_FEATURES,
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build_feature_vector, kmeans_pp, pca_2d,
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name_cluster, risk_score_from_centroid, _mean_vec,
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)
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router = APIRouter(prefix="/api/clustering", tags=["clustering"])
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# ─── Cache en mémoire ─────────────────────────────────────────────────────────
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# Stocke (cluster_id → liste d'IPs) pour le drill-down
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# + timestamp de dernière mise à jour
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_cache: dict = {
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"assignments": {}, # ip+ja4 → cluster_idx
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"cluster_ips": {}, # cluster_idx → [(ip, ja4)]
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"params": {}, # k, ts
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}
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# ─── Couleurs ─────────────────────────────────────────────────────────────────
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_THREAT_COLOR = {
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0.92: "#dc2626", # Bot scanner
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0.70: "#ef4444", # Critique
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0.45: "#f97316", # Élevé
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0.25: "#eab308", # Modéré
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0.00: "#6b7280", # Sain / inconnu
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}
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def _risk_to_color(risk: float) -> str:
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for threshold, color in sorted(_THREAT_COLOR.items(), reverse=True):
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if risk >= threshold:
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return color
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return "#6b7280"
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# ─── SQL ──────────────────────────────────────────────────────────────────────
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_SQL_FEATURES = """
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SELECT
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replaceRegexpAll(toString(t.src_ip), '^::ffff:', '') AS ip,
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t.ja4,
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any(t.tcp_ttl_raw) AS ttl,
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any(t.tcp_win_raw) AS win,
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any(t.tcp_scale_raw) AS scale,
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any(t.tcp_mss_raw) AS mss,
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any(t.first_ua) AS ua,
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sum(t.hits) AS hits,
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avg(abs(ml.anomaly_score)) AS avg_score,
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avg(ml.hit_velocity) AS avg_velocity,
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avg(ml.fuzzing_index) AS avg_fuzzing,
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avg(ml.is_headless) AS pct_headless,
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avg(ml.post_ratio) AS avg_post,
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avg(ml.ip_id_zero_ratio) AS ip_id_zero,
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avg(ml.temporal_entropy) AS entropy,
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avg(ml.modern_browser_score) AS browser_score,
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avg(ml.alpn_http_mismatch) AS alpn_mismatch,
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avg(ml.is_alpn_missing) AS alpn_missing,
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avg(ml.multiplexing_efficiency) AS h2_eff,
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avg(ml.header_order_confidence) AS hdr_conf,
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avg(ml.ua_ch_mismatch) AS ua_ch_mismatch,
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avg(ml.asset_ratio) AS asset_ratio,
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avg(ml.direct_access_ratio) AS direct_ratio,
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avg(ml.distinct_ja4_count) AS ja4_count,
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max(ml.is_ua_rotating) AS ua_rotating,
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max(ml.threat_level) AS threat,
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any(ml.country_code) AS country,
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any(ml.asn_org) AS asn_org
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FROM mabase_prod.agg_host_ip_ja4_1h t
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LEFT JOIN mabase_prod.ml_detected_anomalies ml
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ON t.src_ip = ml.src_ip AND t.ja4 = ml.ja4
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AND ml.detected_at >= now() - INTERVAL 24 HOUR
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WHERE t.window_start >= now() - INTERVAL 24 HOUR
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AND t.tcp_ttl_raw > 0
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GROUP BY t.src_ip, t.ja4
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ORDER BY
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-- Stratégie : IPs anormales en premier, puis fort trafic
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-- Cela garantit que les bots Masscan (anomalie=0.97, hits=1-2) sont inclus
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avg(abs(ml.anomaly_score)) DESC,
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sum(t.hits) DESC
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LIMIT %(limit)s
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"""
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# Noms des colonnes SQL dans l'ordre
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_SQL_COLS = [
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"ip", "ja4", "ttl", "win", "scale", "mss", "ua", "hits",
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"avg_score", "avg_velocity", "avg_fuzzing", "pct_headless", "avg_post",
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"ip_id_zero", "entropy", "browser_score", "alpn_mismatch", "alpn_missing",
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"h2_eff", "hdr_conf", "ua_ch_mismatch", "asset_ratio", "direct_ratio",
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"ja4_count", "ua_rotating", "threat", "country", "asn_org",
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]
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# ─── Endpoints ────────────────────────────────────────────────────────────────
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@router.get("/clusters")
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async def get_clusters(
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k: int = Query(14, ge=4, le=30, description="Nombre de clusters"),
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n_samples: int = Query(3000, ge=500, le=8000, description="Taille de l'échantillon"),
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):
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"""
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Clustering multi-métriques des IPs.
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Retourne les nœuds (clusters) + arêtes pour ReactFlow, avec :
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- positions 2D issues de PCA sur les 21 features
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- profil radar des features par cluster (normalisé [0,1])
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- statistiques détaillées (moyennes brutes des features)
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- sample d'IPs représentatives
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"""
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t0 = time.time()
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try:
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result = db.query(_SQL_FEATURES, {"limit": n_samples})
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"ClickHouse: {e}")
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# ── Construction des vecteurs de features ─────────────────────────────
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rows: list[dict] = []
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for row in result.result_rows:
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d = {col: row[i] for i, col in enumerate(_SQL_COLS)}
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rows.append(d)
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if len(rows) < k:
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raise HTTPException(status_code=400, detail="Pas assez de données pour ce k")
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points = [build_feature_vector(r) for r in rows]
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# ── K-means++ ────────────────────────────────────────────────────────
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km = kmeans_pp(points, k=k, max_iter=60, seed=42)
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# ── PCA-2D sur les centroïdes ─────────────────────────────────────────
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# On projette les centroïdes dans l'espace PCA des données
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# → les positions relatives reflètent la variance des données
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coords_all = pca_2d(points)
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# Moyenne des positions PCA par cluster = position 2D du centroïde
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cluster_xs: list[list[float]] = [[] for _ in range(k)]
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cluster_ys: list[list[float]] = [[] for _ in range(k)]
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for i, label in enumerate(km.labels):
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cluster_xs[label].append(coords_all[i][0])
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cluster_ys[label].append(coords_all[i][1])
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centroid_2d: list[tuple[float, float]] = []
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for j in range(k):
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if cluster_xs[j]:
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cx = sum(cluster_xs[j]) / len(cluster_xs[j])
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cy = sum(cluster_ys[j]) / len(cluster_ys[j])
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else:
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cx, cy = 0.5, 0.5
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centroid_2d.append((cx, cy))
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# ── Agrégation des statistiques par cluster ───────────────────────────
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cluster_rows: list[list[dict]] = [[] for _ in range(k)]
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cluster_members: list[list[tuple[str, str]]] = [[] for _ in range(k)]
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for i, label in enumerate(km.labels):
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cluster_rows[label].append(rows[i])
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cluster_members[label].append((rows[i]["ip"], rows[i]["ja4"]))
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# Mise à jour du cache pour le drill-down
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_cache["cluster_ips"] = {j: cluster_members[j] for j in range(k)}
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_cache["params"] = {"k": k, "ts": t0}
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# ── Construction des nœuds ReactFlow ─────────────────────────────────
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CANVAS_W, CANVAS_H = 1400, 780
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nodes = []
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for j in range(k):
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if not cluster_rows[j]:
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continue
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# Statistiques brutes moyennées
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def avg_feat(key: str) -> float:
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vals = [float(r.get(key) or 0) for r in cluster_rows[j]]
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return sum(vals) / len(vals) if vals else 0.0
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mean_ttl = avg_feat("ttl")
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mean_mss = avg_feat("mss")
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mean_scale = avg_feat("scale")
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mean_win = avg_feat("win")
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mean_score = avg_feat("avg_score")
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mean_vel = avg_feat("avg_velocity")
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mean_fuzz = avg_feat("avg_fuzzing")
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mean_hless = avg_feat("pct_headless")
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mean_post = avg_feat("avg_post")
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mean_asset = avg_feat("asset_ratio")
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mean_direct= avg_feat("direct_ratio")
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mean_alpn = avg_feat("alpn_mismatch")
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mean_h2 = avg_feat("h2_eff")
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mean_hconf = avg_feat("hdr_conf")
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mean_ua_ch = avg_feat("ua_ch_mismatch")
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mean_entr = avg_feat("entropy")
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mean_ja4 = avg_feat("ja4_count")
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mean_ip_id = avg_feat("ip_id_zero")
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mean_brow = avg_feat("browser_score")
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mean_uarot = avg_feat("ua_rotating")
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ip_count = len(set(r["ip"] for r in cluster_rows[j]))
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hit_count = int(sum(float(r.get("hits") or 0) for r in cluster_rows[j]))
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# Pays / ASN / Menace dominants
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threats = [str(r.get("threat") or "") for r in cluster_rows[j] if r.get("threat")]
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countries = [str(r.get("country") or "") for r in cluster_rows[j] if r.get("country")]
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orgs = [str(r.get("asn_org") or "") for r in cluster_rows[j] if r.get("asn_org")]
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def topk(lst: list[str], n: int = 5) -> list[str]:
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from collections import Counter
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return [v for v, _ in Counter(lst).most_common(n) if v]
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raw_stats = {
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"mean_ttl": mean_ttl, "mean_mss": mean_mss,
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"mean_scale": mean_scale,
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}
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label = name_cluster(km.centroids[j], raw_stats)
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risk = risk_score_from_centroid(km.centroids[j])
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color = _risk_to_color(risk)
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# Profil radar normalisé (valeurs centroïde [0,1])
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radar = [
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{"feature": name, "value": round(km.centroids[j][i], 4)}
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for i, name in enumerate(FEATURE_NAMES)
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]
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# Position 2D (PCA normalisée → pixels ReactFlow)
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px_x = centroid_2d[j][0] * CANVAS_W * 0.85 + 80
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px_y = (1 - centroid_2d[j][1]) * CANVAS_H * 0.85 + 50 # inverser y (haut=risque)
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# Rayon ∝ √ip_count
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radius = max(18, min(90, int(math.sqrt(ip_count) * 0.3)))
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# Sample IPs (top 8 par hits)
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sample_rows = sorted(cluster_rows[j], key=lambda r: float(r.get("hits") or 0), reverse=True)[:8]
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sample_ips = [r["ip"] for r in sample_rows]
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sample_ua = str(cluster_rows[j][0].get("ua") or "")
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cluster_id = f"c{j}_k{k}"
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nodes.append({
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"id": cluster_id,
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"label": label,
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"cluster_idx": j,
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"x": round(px_x, 1),
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"y": round(px_y, 1),
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"radius": radius,
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"color": color,
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"risk_score": risk,
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# Caractéristiques TCP
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"mean_ttl": round(mean_ttl, 1),
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"mean_mss": round(mean_mss, 0),
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"mean_scale": round(mean_scale, 1),
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"mean_win": round(mean_win, 0),
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# Comportement HTTP
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"mean_score": round(mean_score, 4),
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"mean_velocity": round(mean_vel, 3),
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"mean_fuzzing": round(mean_fuzz, 3),
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"mean_headless": round(mean_hless, 3),
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"mean_post": round(mean_post, 3),
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"mean_asset": round(mean_asset, 3),
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"mean_direct": round(mean_direct, 3),
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# TLS / Protocole
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"mean_alpn_mismatch": round(mean_alpn, 3),
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"mean_h2_eff": round(mean_h2, 3),
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"mean_hdr_conf": round(mean_hconf, 3),
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"mean_ua_ch": round(mean_ua_ch, 3),
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# Temporel
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"mean_entropy": round(mean_entr, 3),
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"mean_ja4_diversity": round(mean_ja4, 3),
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"mean_ip_id_zero": round(mean_ip_id, 3),
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"mean_browser_score": round(mean_brow, 1),
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"mean_ua_rotating": round(mean_uarot, 3),
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# Meta
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"ip_count": ip_count,
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"hit_count": hit_count,
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"top_threat": topk(threats, 1)[0] if topk(threats, 1) else "",
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"top_countries": topk(countries, 5),
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"top_orgs": topk(orgs, 5),
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"sample_ips": sample_ips,
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"sample_ua": sample_ua,
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# Profil radar pour visualisation
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"radar": radar,
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})
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# ── Arêtes : k-NN dans l'espace des features ──────────────────────────
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# Chaque cluster est connecté à ses 2 voisins les plus proches
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edges = []
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seen: set[frozenset] = set()
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centroids = km.centroids
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for i, ni in enumerate(nodes):
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ci = ni["cluster_idx"]
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# Distance² aux autres centroïdes
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dists = [
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(j, nj["cluster_idx"],
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sum((centroids[ci][d] - centroids[nj["cluster_idx"]][d]) ** 2
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for d in range(N_FEATURES)))
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for j, nj in enumerate(nodes) if j != i
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]
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dists.sort(key=lambda x: x[2])
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# 2 voisins les plus proches
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for j, cj, dist2 in dists[:2]:
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key = frozenset([ni["id"], nodes[j]["id"]])
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if key in seen:
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continue
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seen.add(key)
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similarity = round(1.0 / (1.0 + math.sqrt(dist2)), 3)
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edges.append({
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"id": f"e_{ni['id']}_{nodes[j]['id']}",
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"source": ni["id"],
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"target": nodes[j]["id"],
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"similarity": similarity,
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"weight": round(similarity * 5, 1),
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})
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# ── Stats globales ────────────────────────────────────────────────────
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total_ips = sum(n["ip_count"] for n in nodes)
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total_hits = sum(n["hit_count"] for n in nodes)
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bot_ips = sum(n["ip_count"] for n in nodes if n["risk_score"] > 0.40 or "🤖" in n["label"])
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high_risk = sum(n["ip_count"] for n in nodes if n["risk_score"] > 0.20)
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elapsed = round(time.time() - t0, 2)
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return {
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"nodes": nodes,
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"edges": edges,
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"stats": {
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"total_clusters": len(nodes),
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"total_ips": total_ips,
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"total_hits": total_hits,
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"bot_ips": bot_ips,
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"high_risk_ips": high_risk,
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"n_samples": len(rows),
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"k": k,
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"elapsed_s": elapsed,
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},
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"feature_names": FEATURE_NAMES,
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}
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||||
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@router.get("/cluster/{cluster_id}/ips")
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async def get_cluster_ips(
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cluster_id: str,
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limit: int = Query(100, ge=1, le=500),
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||||
offset: int = Query(0, ge=0),
|
||||
):
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"""
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IPs appartenant à un cluster (depuis le cache de la dernière exécution).
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Si le cache est expiré, retourne une erreur guidant vers /clusters.
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"""
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||||
if not _cache.get("cluster_ips"):
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raise HTTPException(
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||||
status_code=404,
|
||||
detail="Cache expiré — appelez /api/clustering/clusters d'abord"
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||||
)
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||||
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||||
# Extrait l'index cluster depuis l'id (format: c{idx}_k{k})
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||||
try:
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||||
idx = int(cluster_id.split("_")[0][1:])
|
||||
except (ValueError, IndexError):
|
||||
raise HTTPException(status_code=400, detail="cluster_id invalide")
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||||
|
||||
members = _cache["cluster_ips"].get(idx, [])
|
||||
if not members:
|
||||
return {"ips": [], "total": 0, "cluster_id": cluster_id}
|
||||
|
||||
total = len(members)
|
||||
page_members = members[offset: offset + limit]
|
||||
|
||||
# Requête SQL pour les détails de ces IPs spécifiques
|
||||
ip_list = [m[0] for m in page_members]
|
||||
ja4_list = [m[1] for m in page_members]
|
||||
|
||||
if not ip_list:
|
||||
return {"ips": [], "total": total, "cluster_id": cluster_id}
|
||||
|
||||
# On ne peut pas facilement passer une liste en paramètre ClickHouse —
|
||||
# on la construit directement (valeurs nettoyées)
|
||||
safe_ips = [ip.replace("'", "") for ip in ip_list[:100]]
|
||||
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]),
|
||||
"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}
|
||||
Reference in New Issue
Block a user