08d003a050
- Suppression de 'Score Anomalie' (avg_score) des 31→30 features de clustering - Suppression de 'Score de détection robot' (mean_score) de la sidebar et de l'API - Suppression de bot_ips / high_risk_ips des stats (métriques dérivées des scores supprimés) - Redistribution des poids dans risk_score_from_centroid: UA-CH mismatch +17%, fuzzing +14%, headless +10%, vélocité +9%, ip_id_zero +7% - Mise à jour des indices feature dans name_cluster et risk_score_from_centroid - Palette 24 couleurs spectrales (cluster_color) → bleu/violet/rose/teal/amber/cyan/lime... Les couleurs identifient les clusters, non leur niveau de risque - Remplacement de la légende CRITICAL/HIGH/MEDIUM/LOW par la liste des clusters actifs - Ajout de spread_clusters(): répulsion itérative des centroïdes trop proches (50 iter) min_dist=0.16 → les clusters se repoussent mutuellement → visualisation plus lisible - Interface TypeScript mise à jour (suppression mean_score, bot_ips, high_risk_ips) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
558 lines
24 KiB
Python
558 lines
24 KiB
Python
"""
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Clustering d'IPs multi-métriques — WebGL / deck.gl backend.
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- Calcul sur la TOTALITÉ des IPs (GROUP BY src_ip, ja4 sans LIMIT)
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- K-means++ vectorisé (numpy) + PCA-2D + enveloppes convexes (scipy)
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- Calcul en background thread + cache 30 min
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- Endpoints : /clusters, /status, /cluster/{id}/points
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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 logging
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import threading
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from collections import Counter
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from concurrent.futures import ThreadPoolExecutor
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from typing import Optional, Any
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import numpy as np
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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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FEATURE_KEYS, FEATURE_NAMES, FEATURE_NORMS, N_FEATURES,
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build_feature_vector, kmeans_pp, pca_2d, compute_hulls,
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name_cluster, risk_score_from_centroid, standardize,
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cluster_color, spread_clusters,
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)
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log = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/clustering", tags=["clustering"])
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# ─── Cache global ──────────────────────────────────────────────────────────────
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_CACHE: dict[str, Any] = {
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"status": "idle", # idle | computing | ready | error
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"error": None,
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"result": None, # dict résultat complet
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"ts": 0.0, # timestamp dernière mise à jour
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"params": {},
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"cluster_ips": {}, # cluster_idx → [(ip, ja4, pca_x, pca_y, risk)]
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}
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_CACHE_TTL = 1800 # 30 minutes
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_LOCK = threading.Lock()
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_EXECUTOR = ThreadPoolExecutor(max_workers=1, thread_name_prefix="clustering")
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# ─── Palette de couleurs (remplace l'ancienne logique menace) ─────────────────
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# Les couleurs sont désormais attribuées par index de cluster pour maximiser
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# la distinction visuelle, indépendamment du niveau de risque.
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# ─── SQL : TOUTES les IPs sans LIMIT ─────────────────────────────────────────
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_SQL_ALL_IPS = """
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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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-- Features headers HTTP (depuis view_dashboard_entities)
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avg(ml.has_accept_language) AS hdr_accept_lang,
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any(vh.hdr_enc) AS hdr_has_encoding,
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any(vh.hdr_sec_fetch) AS hdr_has_sec_fetch,
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any(vh.hdr_count) AS hdr_count_raw,
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-- Fingerprint HTTP Headers (depuis agg_header_fingerprint_1h + ml_detected_anomalies)
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-- header_order_shared_count : nb d'IPs partageant le même fingerprint
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-- → faible = fingerprint rare = comportement suspect
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avg(ml.header_order_shared_count) AS hfp_shared_count,
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-- distinct_header_orders : nb de fingerprints distincts émis par cette IP
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-- → élevé = rotation de fingerprint = comportement bot
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avg(ml.distinct_header_orders) AS hfp_distinct_orders,
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-- Cookie et Referer issus de la table dédiée aux empreintes
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any(hfp.hfp_cookie) AS hfp_cookie,
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any(hfp.hfp_referer) AS hfp_referer
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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 %(hours)s HOUR
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LEFT JOIN (
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SELECT
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toIPv6(concat('::ffff:', toString(src_ip))) AS src_ip_v6,
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ja4,
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any(arrayExists(x -> x LIKE '%%Accept-Encoding%%', client_headers)) AS hdr_enc,
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any(arrayExists(x -> x LIKE '%%Sec-Fetch%%', client_headers)) AS hdr_sec_fetch,
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any(length(splitByChar(',', client_headers[1]))) AS hdr_count
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FROM mabase_prod.view_dashboard_entities
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WHERE length(client_headers) > 0
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AND log_date >= today() - 2
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GROUP BY src_ip_v6, ja4
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) vh ON t.src_ip = vh.src_ip_v6 AND t.ja4 = vh.ja4
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LEFT JOIN (
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SELECT
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src_ip,
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avg(has_cookie) AS hfp_cookie,
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avg(has_referer) AS hfp_referer
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FROM mabase_prod.agg_header_fingerprint_1h
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WHERE window_start >= now() - INTERVAL %(hours)s HOUR
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GROUP BY src_ip
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) hfp ON t.src_ip = hfp.src_ip
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WHERE t.window_start >= now() - INTERVAL %(hours)s 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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"""
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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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"hdr_accept_lang", "hdr_has_encoding", "hdr_has_sec_fetch", "hdr_count_raw",
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"hfp_shared_count", "hfp_distinct_orders", "hfp_cookie", "hfp_referer",
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]
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# ─── Worker de clustering (thread pool) ──────────────────────────────────────
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def _run_clustering_job(k: int, hours: int, sensitivity: float = 1.0) -> None:
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"""Exécuté dans le thread pool. Met à jour _CACHE.
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sensitivity : multiplicateur de k [0.5 – 5.0].
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0.5 = vue très agrégée (k/2 clusters)
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1.0 = comportement par défaut
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2.0 = deux fois plus de clusters → groupes plus homogènes
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5.0 = granularité maximale (classification la plus fine)
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k_actual est plafonné à 300 pour éviter des temps de calcul excessifs.
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n_init est réduit à 1 quand k_actual > 60 pour rester rapide.
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"""
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k_actual = max(4, min(300, round(k * sensitivity)))
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t0 = time.time()
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with _LOCK:
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_CACHE["status"] = "computing"
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_CACHE["error"] = None
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try:
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log.info(f"[clustering] Démarrage k={k_actual} (base={k}×sens={sensitivity}) hours={hours}")
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# ── 1. Chargement de toutes les IPs ──────────────────────────────
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result = db.query(_SQL_ALL_IPS, {"hours": hours})
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rows: list[dict] = []
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for row in result.result_rows:
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rows.append({col: row[i] for i, col in enumerate(_SQL_COLS)})
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n = len(rows)
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log.info(f"[clustering] {n} IPs chargées")
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if n < k_actual:
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raise ValueError(f"Seulement {n} IPs disponibles (k={k_actual} requis)")
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# ── 2. Construction de la matrice de features (numpy) ────────────
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X = np.array([build_feature_vector(r) for r in rows], dtype=np.float32)
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log.info(f"[clustering] Matrice X: {X.shape} — {X.nbytes/1024/1024:.1f} MB")
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# ── 3. Standardisation z-score ────────────────────────────────────
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# Normalise par variance : features discriminantes (forte std)
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# contribuent plus que les features quasi-constantes.
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X64 = X.astype(np.float64)
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X_std, feat_mean, feat_std = standardize(X64)
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# ── 4. K-means++ sur l'espace standardisé ────────────────────────
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# n_init réduit à 1 pour k élevé (> 60) afin de limiter le temps de calcul
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n_init = 1 if k_actual > 60 else 3
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km = kmeans_pp(X_std, k=k_actual, max_iter=80, n_init=n_init, seed=42)
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log.info(f"[clustering] K-means: {km.n_iter} iters, inertia={km.inertia:.2f}")
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# Centroïdes dans l'espace original [0,1] pour affichage radar
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# (dé-standardisation : c_orig = c_std * std + mean, puis clip [0,1])
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centroids_orig = np.clip(km.centroids * feat_std + feat_mean, 0.0, 1.0)
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# ── 5. PCA-2D sur les features ORIGINALES (normalisées [0,1]) ────
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coords = pca_2d(X64) # (n, 2), normalisé [0,1]
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# ── 5b. Dispersion — repousse les clusters trop proches ──────────
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coords = spread_clusters(coords, km.labels, k_actual,
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n_iter=60, min_dist=0.16)
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# ── 5c. Enveloppes convexes par cluster ──────────────────────────
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hulls = compute_hulls(coords, km.labels, k_actual)
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# ── 6. Agrégation par cluster ─────────────────────────────────────
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cluster_rows: list[list[dict]] = [[] for _ in range(k_actual)]
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cluster_coords: list[list[list[float]]] = [[] for _ in range(k_actual)]
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cluster_ips_map: dict[int, list] = {j: [] for j in range(k_actual)}
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for i, label in enumerate(km.labels):
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j = int(label)
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cluster_rows[j].append(rows[i])
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cluster_coords[j].append(coords[i].tolist())
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cluster_ips_map[j].append((
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rows[i]["ip"],
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rows[i]["ja4"],
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float(coords[i][0]),
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float(coords[i][1]),
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float(risk_score_from_centroid(centroids_orig[j])),
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))
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# ── 7. Construction des nœuds ─────────────────────────────────────
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nodes = []
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for j in range(k_actual):
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if not cluster_rows[j]:
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continue
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def avg_f(key: str, crows: list[dict] = cluster_rows[j]) -> float:
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return float(np.mean([float(r.get(key) or 0) for r in crows]))
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mean_ttl = avg_f("ttl")
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mean_mss = avg_f("mss")
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mean_scale = avg_f("scale")
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mean_win = avg_f("win")
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raw_stats = {"mean_ttl": mean_ttl, "mean_mss": mean_mss, "mean_scale": mean_scale}
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label_name = name_cluster(centroids_orig[j], raw_stats)
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risk = float(risk_score_from_centroid(centroids_orig[j]))
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color = cluster_color(j)
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# Centroïde 2D = moyenne des coords du cluster
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cxy = np.mean(cluster_coords[j], axis=0).tolist() if cluster_coords[j] else [0.5, 0.5]
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ip_set = list({r["ip"] for r in cluster_rows[j]})
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ip_count = len(ip_set)
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hit_count = int(sum(float(r.get("hits") or 0) for r in cluster_rows[j]))
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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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return [v for v, _ in Counter(lst).most_common(n) if v]
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radar = [
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{"feature": name, "value": round(float(centroids_orig[j][i]), 4)}
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for i, name in enumerate(FEATURE_NAMES)
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]
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radius = max(8, min(30, int(math.log1p(ip_count) * 2.2)))
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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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nodes.append({
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"id": f"c{j}_k{k_actual}",
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"cluster_idx": j,
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"label": label_name,
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"pca_x": round(cxy[0], 6),
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"pca_y": round(cxy[1], 6),
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"radius": radius,
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"color": color,
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"risk_score": round(risk, 4),
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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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"mean_velocity":round(avg_f("avg_velocity"),3),
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"mean_fuzzing": round(avg_f("avg_fuzzing"), 3),
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"mean_headless":round(avg_f("pct_headless"),3),
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"mean_post": round(avg_f("avg_post"), 3),
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"mean_asset": round(avg_f("asset_ratio"), 3),
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"mean_direct": round(avg_f("direct_ratio"),3),
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"mean_alpn_mismatch": round(avg_f("alpn_mismatch"),3),
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"mean_h2_eff": round(avg_f("h2_eff"), 3),
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"mean_hdr_conf":round(avg_f("hdr_conf"), 3),
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"mean_ua_ch": round(avg_f("ua_ch_mismatch"),3),
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"mean_entropy": round(avg_f("entropy"), 3),
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"mean_ja4_diversity": round(avg_f("ja4_count"),3),
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"mean_ip_id_zero": round(avg_f("ip_id_zero"),3),
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"mean_browser_score": round(avg_f("browser_score"),1),
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"mean_ua_rotating": round(avg_f("ua_rotating"),3),
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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 threats 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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"radar": radar,
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# Hull pour deck.gl PolygonLayer
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"hull": hulls.get(j, []),
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})
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# ── 8. Arêtes k-NN entre clusters ────────────────────────────────
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edges = []
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seen: set[frozenset] = set()
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for i, ni in enumerate(nodes):
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ci = ni["cluster_idx"]
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dists = sorted(
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[(j, nj["cluster_idx"],
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float(np.sum((centroids_orig[ci] - centroids_orig[nj["cluster_idx"]]) ** 2)))
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for j, nj in enumerate(nodes) if j != i],
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key=lambda x: x[2]
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)
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for j_idx, cj, d2 in dists[:2]:
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key = frozenset([ni["id"], nodes[j_idx]["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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edges.append({
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"id": f"e_{ni['id']}_{nodes[j_idx]['id']}",
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"source": ni["id"],
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"target": nodes[j_idx]["id"],
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"similarity": round(1.0 / (1.0 + math.sqrt(d2)), 3),
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})
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# ── 9. Stockage résultat + cache IPs ─────────────────────────────
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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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elapsed = round(time.time() - t0, 2)
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result_dict = {
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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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"n_samples": n,
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"k": k_actual,
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"k_base": k,
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"sensitivity": sensitivity,
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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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with _LOCK:
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_CACHE["result"] = result_dict
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_CACHE["cluster_ips"] = cluster_ips_map
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_CACHE["status"] = "ready"
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_CACHE["ts"] = time.time()
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_CACHE["params"] = {"k": k, "hours": hours, "sensitivity": sensitivity}
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_CACHE["error"] = None
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log.info(f"[clustering] Terminé en {elapsed}s — {total_ips} IPs, {len(nodes)} clusters")
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except Exception as e:
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log.exception("[clustering] Erreur lors du calcul")
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with _LOCK:
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_CACHE["status"] = "error"
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_CACHE["error"] = str(e)
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def _maybe_trigger(k: int, hours: int, sensitivity: float) -> None:
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"""Lance le calcul si cache absent, expiré ou paramètres différents."""
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with _LOCK:
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status = _CACHE["status"]
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params = _CACHE["params"]
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ts = _CACHE["ts"]
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cache_stale = (time.time() - ts) > _CACHE_TTL
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params_changed = (
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params.get("k") != k or
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params.get("hours") != hours or
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params.get("sensitivity") != sensitivity
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)
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if status in ("computing",):
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return # déjà en cours
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if status == "ready" and not cache_stale and not params_changed:
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return # cache frais
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|
||
_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}
|