Files
dashboard/backend/routes/investigation_summary.py
SOC Analyst e2db8ca84e 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>
2026-03-18 18:22:57 +01:00

182 lines
8.3 KiB
Python

"""
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")
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))