Commit Graph

62 Commits

Author SHA1 Message Date
a1e4c1dad5 feat: add ja4ebpf service — eBPF-based TLS/TCP fingerprinting daemon
- TC ingress hook captures TCP SYN (L3/L4) and TLS ClientHello
- Uprobes on SSL_read/SSL_set_fd capture decrypted TLS data
- Kprobes on accept4 correlate socket FDs to client IP:port
- JA4 fingerprint computed from parsed TLS ClientHello
- HTTP/2 SETTINGS and WINDOW_UPDATE extracted from decrypted streams
- Session manager with sharded map (256 shards) and GC goroutine
- Slowloris detection: sessions with no requests after 10s threshold
- ClickHouse batch writer to ja4_logs.http_logs_raw (raw_json)
- All tests pass: 17 parser + 10 correlation tests

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-11 22:43:26 +02:00
7eb3ad21fd feat(dashboard): afficher SETTINGS H2 individuels dans la table mismatch
- /api/browser-signatures : top_mismatches inclut désormais les 7 colonnes
  SETTINGS individuelles (h2_header_table_size, h2_enable_push,
  h2_max_concurrent_streams, h2_initial_window_size, h2_max_frame_size,
  h2_max_header_list_size, h2_enable_connect_protocol)
- stats : ajout sessions_with_priority (countIf h2_priority_present > 0)
- browsers.html : colonne SETTINGS compact dans la table suspects
  (format '3:100, 4:65536, 2:0' — IDs Akamai avec valeurs non-nulles)
- Compteur pseudo-priority utilise la vraie valeur sessions_with_priority
  au lieu d'afficher '—'

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-11 03:11:17 +02:00
f704541f83 feat(h2): direct per-parameter SETTINGS comparison in browser_matcher
- Rewrote _d1_h2_settings() with 3-signal weighted formula:
  direct_score×0.60 + dict_match×0.30 + ja4_coherence×0.10
  when individual SETTINGS cols are available in the DataFrame
- Added _H2_SETTINGS_COLS dict (IDs 1,2,3,4,5,6,8 → column names)
- Fallback to dict_match×0.80 + ja4_coherence×0.20 for backward compat
- Fix view_ai_features_1h: pass 7 individual SETTINGS columns through
  base_data CTE (h2_header_table_size, h2_enable_push,
  h2_max_concurrent_streams, h2_initial_window_size, h2_max_frame_size,
  h2_max_header_list_size, h2_enable_connect_protocol)
- Remove non-existent h2_dict_confidence reference from view SQL
  (dict_browser_h2 only exposes browser_family attribute)
- Add 7 new pytest cases: exact match, one wrong setting, forbidden key
  penalty, unknown fingerprint with correct settings, fallback path,
  CDN proxy neutralisation, full Chrome simulation
- 53/53 bot-detector tests pass
- Update thesis §3.9.2: document direct comparison algorithm + fallback

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-11 03:05:36 +02:00
85d3b95b7b feat: HTTP/2 passive fingerprinting with individual SETTINGS fields
Complete implementation of HTTP/2 passive fingerprinting per thesis §2.5.3:

mod-reqin-log (C module):
- Replace connection-level filter with ap_hook_process_connection (APR_HOOK_FIRST)
  to capture H2 preface before mod_http2 takes over the connection
- AP_MODE_SPECULATIVE read of 512 bytes from c->input_filters
- Parse SETTINGS, WINDOW_UPDATE, PRIORITY flags, pseudo-header order
- Output individual SETTINGS params as separate JSON fields (IDs 1-6, 8)
- Read H2 notes from c1 (master connection) for mod_http2 secondary conns
- Fix header_order_signature JSON length bug (26→strlen)

ClickHouse schema:
- Add 8 new columns to http_logs: h2_has_priority, h2_header_table_size,
  h2_enable_push, h2_max_concurrent_streams, h2_initial_window_size,
  h2_max_frame_size, h2_max_header_list_size, h2_enable_connect_protocol
- Use Int32/Int64 with DEFAULT -1 to distinguish absent vs zero
- Update mv_http_logs to extract individual fields via JSONHas/JSONExtractInt
- Migration 04_http2_fields.sql updated for existing deployments

Correlator:
- Accept both timestamp_ns and timestamp field names (backward compat)

Integration:
- Enable HTTP/2 in Apache: Protocols h2 http/1.1 in httpd-integration.conf

Validated end-to-end via Playwright: H2 curl traffic → mod-reqin-log →
correlator → ClickHouse with all 12 H2 columns populated correctly.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-11 02:33:45 +02:00
d098de1a66 fix(bot-detector): neutralize H2 dimensions behind proxy (X-Forwarded-For)
When has_xff=1, the H2 connection is terminated by the reverse proxy/CDN,
so client H2 fingerprints are lost. Previously only D1 (h2_settings) was
neutralized; D2 (window_update), D3 (pseudo_order), and D4 (priority)
still penalized proxied traffic — a real Chrome behind Cloudflare scored
0.0 on 3 dimensions (45% of total weight).

Now all 4 H2 dimensions return 0.5 (neutral) when has_xff>0, and
non-browser H2 detection is also disabled behind proxies.

Tests: 10/10 passed including 3 new XFF-specific cases.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 15:15:20 +02:00
261205028d fix(dashboard): campaigns scatter chart — show campaigns not IPs
- API /api/campaigns/scatter: aggregate by campaign_id instead of per-IP
  Returns avg_score, avg_velocity, unique_ips, ja4_list, asn_list, country_list
- Template: one bubble per campaign, sized by IP count
- Tooltip: campaign-level info (IPs, score, velocity, ASNs, pays, JA4s)
- Click navigates to campaign detail (not IP detail)
- Updated doc panel text

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 15:09:02 +02:00
fb73c60e7d feat(dashboard): fingerprint discovery page — extract and group JA4/H2/headers from traffic
- GET /api/fingerprint-discovery: queries http_logs, groups by JA4, aggregates
  UA family, header presence rates (Sec-CH-UA, Sec-Fetch, Accept-Language,
  zstd, brotli, gzip, XFF), H2 data, TLS info, dict lookups
- /fingerprints page: KPIs, doughnut chart by family, stacked header bars,
  filterable/sortable profile table, expandable detail panel
- Promote button: push H2 fingerprints to browser_h2_signatures via existing
  POST /api/browser-signatures/entries endpoint
- Nav link: Découverte added after Navigateurs in sidebar

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 15:02:53 +02:00
fde6864311 feat(dashboard): browser signatures management UI
- Ajoute dict_browser_h2 dans /reflists (lecture seule via dict_browser_h2)
- Nouveaux endpoints API :
    GET  /api/browser-signatures/entries — liste browser_h2_signatures
         (fallback dict CSV si migration 06 non appliquée)
    POST /api/browser-signatures/entries — ajout fingerprint + reload dict
    DELETE /api/browser-signatures/entries — suppression + reload dict
- Page /browsers : 2 nouvelles sections
    'Base de signatures H2' — tableau des 10 fingerprints, form d'ajout,
    mode lecture seule automatique si migration 06 non appliquée
    'Règles de scoring browser_matcher.py' — tableau statique des 7 dimensions
    (poids, valeurs par famille, seuils de bypass)
- Integration : browser_h2.csv copié dans user_files au démarrage ClickHouse

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 14:46:07 +02:00
da1b579d4f fix(dashboard): rename duplicate /api/browsers route to /api/browser-signatures
La route /api/browsers existait déjà (distribution JA4 par famille).
La nouvelle route du browser_matcher était en conflit — FastAPI utilisait
la première définition. Renommage en /api/browser-signatures.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 14:17:38 +02:00
9c308747bd feat(dashboard): page Browser Signature Detection (/browsers)
Nouvelle page dédiée à l'analyse passive des signatures navigateur (§4) :

API — GET /api/browsers :
  Requête view_ai_features_1h pour :
  - Compteurs globaux (total, sessions_with_h2, matched, mismatch %)
  - Distribution h2_dict_family (Chrome/Firefox/Safari/Edge)
  - Répartition des signaux WINDOW_UPDATE (chrome/firefox/safari/absent/autre)
  - Mismatch TLS↔H2 par famille JA4 (total + count + %)
  - Top 20 sessions suspectes (tls_h2_family_mismatch=1, triées par hits)

Page /browsers :
  - 6 KPI header (sessions, avec H2, famille connue, taux match, mismatch, % mismatch)
  - Doc banner expliquant browser_matcher §4 et le mode DUAL_MODE
  - Donut : familles H2 (dict_browser_h2 lookup)
  - Bar horizontal : WINDOW_UPDATE signals par famille
  - Bar groupé + ligne : mismatch TLS↔H2 par famille JA4 (count + %)
  - Table : top 20 imposteurs potentiels avec IP cliquable, pseudo-order, cohérence
  - Mini-KPIs : ordres pseudo-headers Chrome/Safari, Firefox, inconnu, PRIORITY frames
  - Lien nav 'Navigateurs' dans le groupe Surveillance de base.html

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 14:02:39 +02:00
e52cdcc01f feat(bot-detector): Browser Signature Detection engine (parallel mode)
Étape A — browser_signatures.py
  Données pures : BROWSER_SIGNATURES (Chrome/Firefox/Safari), NON_BROWSER_SIGNATURES
  (curl/httpx/go), BROWSER_THRESHOLDS, DIMENSION_WEIGHTS. Valeurs H2 extraites
  des captures réelles (format Akamai avec virgules, non semicolons).

Étape B — browser_matcher.py
  Moteur vectorisé 7 dimensions (H2 SETTINGS 0.30, WINDOW_UPDATE 0.15,
  pseudo-header order 0.15, H2 PRIORITY 0.10, HTTP headers 0.15, TLS 0.10,
  JA4 dict 0.05). run_browser_matcher(df) ajoute bm_family/bm_score/bm_decision.
  CDN edge case : dimension H2 neutralisée (0.5) si has_xff=1.
  BROWSER_MATCHER_REPLACE=false par défaut (mode DUAL_MODE logging uniquement).

Étape C — 06_browser_signature_detection.sql (migration)
  Crée browser_h2_signatures (table MergeTree avec 12 fingerprints de référence).
  Recrée dict_browser_h2 depuis la table avec champ confidence (remplace CSV).

Étape D — 07_ai_features_view.sql
  +h2_wu_val dans le JOIN http_logs, +h2_window_update_value, +h2_dict_family,
  +h2_dict_confidence, +h2_window_{chrome,firefox,safari,absent},
  +h2_order_{chromesafari,firefox}, +h2_priority_present, +h2_pseudo_ord_raw,
  +tls_h2_family_mismatch (détection incohérence famille JA4 vs famille H2).

Étape E — preprocessing.py + pipeline.py
  preprocessing.py: appelle run_browser_matcher() après compute_browser_axes(),
  ajoute 7 nouvelles features binaires H2 à FEATURES et binary_features.
  pipeline.py: appelle log_dual_mode_comparison() après la classification A9.
  BROWSER_MATCHER_REPLACE=true active le remplacement du bypass.

Étape F — test_browser_matcher.py
  8 tests : Chrome/Firefox/Safari full match, curl rejeté, httpcloak partiel,
  TLS↔H2 mismatch, CDN proxy neutralisation, go net/http rejeté.
  Tous 8 PASSED (+ 36 tests existants inchangés).

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 13:52:57 +02:00
79dbb23d6f feat(dashboard): sélecteur de plage temporelle sur /campaigns
Avant : toutes les vues de campagnes étaient fixes à 7 jours.
Après : sélecteur 1j / 7j (défaut) / 14j / 30j / 90j en haut à droite.

- Ajout du paramètre ?days= (1–90, défaut 7) à :
    GET /api/campaigns
    GET /api/campaigns/graph
    GET /api/campaigns/scatter
    GET /api/campaigns/{cid}
- Le sélecteur recharge simultanément les 3 vues (cartes, scatter, graphe)
  et le panneau de détail avec la même fenêtre temporelle
- Le compteur de campagnes indique la plage active : (4 campagnes — 30j)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 13:24:08 +02:00
9548b1782d fix: corriger ORDER BY ml_detected_anomalies dans le schéma de base
CH 24.8 refuse MODIFY ORDER BY sur des colonnes existantes (erreur BAD_ARGUMENTS 36).
La migration 01 ne pouvait donc pas corriger l'ORDER BY en post-init.

Correctif :
- 06_ml_tables.sql : ORDER BY (src_ip) → ORDER BY (src_ip, ja4, host, model_name)
  + TTL 30j → 7j (cohérent avec l'architecture documentée)
- 01_ttl_adjustments.sql : supprime le MODIFY ORDER BY impossible, conserve
  uniquement les MODIFY TTL (valides pour les déploiements existants)

Résultat : make init-stack sans aucun ⚠ ni ✗

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 01:34:07 +02:00
92432085e2 fix(campaigns): fix IP navigation URL encoding
fmtIP() returns an HTML <a> tag string. Using encodeURIComponent(fmtIP(ip))
was URL-encoding the entire HTML markup instead of the raw IP address,
resulting in /ip/%3Ca%20href%3D... navigation.

Fix: extract raw IP (stripping ::ffff: prefix) before building the URL.
Applied to all 3 click handlers in campaigns.html:
- members table row onclick
- scatter chart point click
- force graph node click

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 01:08:53 +02:00
7a04e47041 fix(sql+api): fix view column mismatches and ClickHouse 24.8 JOIN issue
- view_form_bruteforce_detected: add post_count, distinct_paths, first_seen, last_seen
- view_host_ip_ja4_rotation: add host, distinct_ja4, ja4_list, window_start
- Replace uniqExact/groupUniqArray with count()/groupArray (no nested-agg error)
- api.py campaigns/graph: move a.src_ip < b.src_ip from JOIN ON to WHERE
  (ClickHouse 24.8 forbids cross-table inequality in JOIN ON condition)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 01:05:04 +02:00
2f2c5e03bb fix(sql): contournement bug scope ClickHouse 24.8 dans view_ai_features_1h
- Restructure 07_ai_features_view.sql : single anonymous inner subquery
  avec aliases explicites sur toutes les colonnes (a.xxx AS xxx, h.xxx AS xxx,
  h2.xxx AS xxx) pour résoudre l'ambiguïté PARTITION BY src_ip dans l'outer SELECT
- Supprime les CTEs multiples (h2_agg, enriched) qui déclenchaient le bug
- Fix migration 04_http2_fields.sql : ordre DEFAULT avant CODEC (syntax ClickHouse)
- make init-stack : 0 erreur sur 13 fichiers SQL

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 00:48:05 +02:00
a108814a56 feat: roadmap détection bots §2-9 — HTTP/2, cohérence, drift, flotte, Jaccard, ExIFFI, méta-learner, métriques
Étape 2 — Fingerprinting HTTP/2 dans le pipeline ML :
- Ajout du dictionnaire dict_browser_h2 (11 familles de navigateurs) dans 05_aggregation_tables.sql
- Ajout du CTE h2_agg et 4 features HTTP/2 dans 07_ai_features_view.sql :
  h2_settings_known, h2_pseudo_order_match, h2_ja4_coherence, h2_settings_rare
- Calcul du fingerprint_coherence_score (5 axes pondérés) dans la vue
- Ajout du 6e axe axis_h2_coherence dans browser.py (poids rééquilibrés)
- browser_h2.csv : 11 fingerprints Akamai → famille navigateur

Étape 3 — Pré-filtre de cohérence sur la baseline humaine :
- pipeline.py exclut les sessions avec fingerprint_coherence_score < seuil de la baseline d'entraînement
- FINGERPRINT_COHERENCE_THRESHOLD configurable via env (défaut 0.25)
- Log des sessions exclues pour analyse SOC

Étape 4 — Détection de drift améliorée :
- scoring.py : passage de 5 à 9 quantiles (p5…p95)
- Ajout de la divergence KL en complément du test KS
- Détection de drift adversarial (≥80% des features dérivent dans la même direction)
- Split temporel strict pour la validation

Étape 5 — Graphe bipartite JA4×ASN (§5.2) :
- fleet.py : détection de flottes via NetworkX + Louvain (imports optionnels)
- enrich_with_fleet_score() : ajout fleet_score + fleet_campaign_flag au DataFrame
- cycle.py : appel après preprocess_df avec log du nombre de sessions en flotte
- SQL migration 05_fleet_metrics_tables.sql : table fleet_detections (TTL 7j)
- Dashboard : /fleet + /api/fleet (communautés détectées) + template fleet.html

Étape 6 — Cross-domain Jaccard §5.8 :
- 12_thesis_features.sql : CTE jaccard_paths → cross_domain_path_similarity
- Signal : même chemins (/admin, /wp-login) sur plusieurs hosts = scanner

Étape 7 — ExIFFI + erreurs AE par feature :
- scoring.py : compute_exiffi_importance() par permutation, compute_ae_feature_errors()
- pipeline.py : calcul ExIFFI sur X_test, mapping index → dict pour anomalies
- build_reason() enrichi avec exiffi_top quand SHAP inactif

Étape 8 — Méta-learner pour la pondération de l'ensemble :
- scoring.py : classe MetaLearner (LogisticRegression, fallback poids fixes <1000 labels)
- Collecte des labels depuis le cycle courant (known_bots, légitimes, Anubis)
- pipeline.py : remplacement des poids fixes par MetaLearner.predict()

Étape 9 — Métriques de performance et monitoring :
- metrics.py : record_cycle_metrics() — taux anomalie, drift, corrélation, latence
- SQL migration 05_fleet_metrics_tables.sql : table ml_performance_metrics (TTL 90j)
- Dashboard : /health + /api/health + template health.html
- cycle.py : appel record_cycle_metrics en fin de cycle (Complet + Applicatif)

Tests : 36/36 bot-detector tests passent

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-10 00:11:35 +02:00
8ca4a1e849 feat(mod_reqin_log): fingerprinting HTTP/2 passif (Akamai format)
Ajoute un filtre d'entrée de connexion (AP_FTYPE_CONNECTION, APR_HOOK_LAST)
qui s'insère entre mod_ssl et mod_http2 pour lire de manière non-destructive
le preface HTTP/2 (RFC 9113 §3.4) et en extraire :

- h2_fingerprint    : fingerprint Akamai complet
                      ex. '1:65536,2:0,4:6291456,6:262144|15663105|0|m,a,s,p'
- h2_settings_fp    : entrées SETTINGS brutes  (ex. '1:65536,4:6291456')
- h2_window_update  : incrément WINDOW_UPDATE  (ex. '15663105')
- h2_pseudo_order   : ordre des pseudo-headers (ex. 'm,a,s,p' Chrome,
                                                     'm,p,s,a' Firefox)

Technique : lecture spéculative AP_MODE_SPECULATIVE (non-destructive)
de 512 octets — la donnée reste disponible pour mod_http2. Le filtre
se retire de la chaîne après la première invocation.

Stockage dans c->notes (H2_NOTE_*) puis émission JSON dans log_request().
ClickHouse : 4 nouvelles colonnes dans http_logs + JSONExtract dans mv_http_logs.
Migration pour déploiements existants : 04_http2_fields.sql.
14 tests unitaires (cmocka) couvrent Chrome/Firefox/HTTP1/troncature/HPACK.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 23:46:50 +02:00
14db3d9040 refactor: suppression dépendance User-Agent de la détection navigateur
Changements SQL :
- modern_browser_score : sec-ch-ua→100, Sec-Fetch→70 (plus de UA fallback)
- Ajout has_sec_ch_ua (UInt8) dans agg_header_fingerprint_1h et ml_all_scores
- mss_mobile_mismatch utilise has_sec_ch_ua au lieu de modern_browser_score
- header_order_confidence : PARTITION BY ja4 au lieu de first_ua
- sec_ch_mobile_mismatch : comparaison Client Hints interne (sans UA)
- Migration 03_remove_ua_browser_detection.sql

Changements Python :
- browser.py Axe 3 : Client Hints + Sec-Fetch + is_fake_navigation (PAS de UA)
- Pondération axes : ja4_known 0.30, tls_coherence 0.20 (signaux TLS renforcés)
- preprocessing.py : has_sec_ch_ua ajouté aux features et binary_features

Fichiers modifiés : 8 SQL/Python + 1 migration, 36/36 tests passent.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 23:06:01 +02:00
00e99e5464 fix(bot-detector): make scoring functions public (remove underscore prefix)
compute_shap_top_features, build_reason, cluster_anomalies renamed from
private (_prefixed) to public to match pipeline.py imports.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 22:49:48 +02:00
629f7b334d fix(bot-detector): rename _compute_drift_score to public, fix import
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 22:48:21 +02:00
de6d8da931 fix(bot-detector): FEATURES_BASE → FEATURES import name mismatch
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 22:42:32 +02:00
6d64c2a8a8 fix(rpm): add systemd-rpm-macros to Dockerfile.package, fix correlator spec_version
- sentinel/correlator: install systemd-rpm-macros in rpm-builder stage
- correlator: use build_version macro (not version) to avoid recursive expansion
- mod-reqin-log: fix ctest --test-dir to find tests in build/tests/

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 22:33:53 +02:00
6b3cc54652 docs: réécriture audit, DOCUMENTATION.md et IMPROVEMENTS.md pour architecture modulaire
- AUDIT: conformité mise à jour 97.9% (142/145), références modulaires
- DOCUMENTATION.md: 1083 lignes, 7 sections, 11 modules documentés
- IMPROVEMENTS.md: A1-A10/B1-B10 annotés /🔄/ avec localisations

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 22:14:18 +02:00
9ea36ad22e feat(scripts): complete stack init + prod data import with date shift
Schema cleanup:
- Remove anubis_ua_rules table stub from 03_anubis_tables.sql
- Remove anubis_ua_rules from bot-detector deploy_schema.sql
- Remove UA seed step from clickhouse-init.sh (no more REGEXP_TREE dependency)
- Drop dict_anubis_ua, dict_anubis_country, anubis_ua_rules, anubis_country_rules

New scripts:
- scripts/init-stack.sh: comprehensive ClickHouse init (13 SQL files + migrations
  + validation + cleanup of obsolete tables). Supports --reset, --import-prod.
- scripts/import-prod-data.sh: imports pre-exported prod data (Native format)
  with dynamic date shift (max(time) → now). Supports --shift, --no-truncate.
- scripts/data/prod-export/: directory for cached Native format exports

Makefile targets: init-stack, import-prod-data, init-and-import

Tested: init-stack.sh passes all 13 SQL + 7 critical tables + 7 dicts
        import-prod-data.sh: 3M rows in ~37s with auto date shift
        Dashboard: 55 routes OK, bot-detector: 36/36 tests pass

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 21:40:05 +02:00
8180f4af04 refactor(anubis): simplify to IP/CIDR + ASN only, remove UA and Country rules
- Remove UA regex extraction (extract_ua_regex, _extract_ua_from_all/any)
- Remove Country rule collection from parse_bot_policies_inline
- Simplify fetch_rules.py: collect_all_rules returns (ip_rules, asn_rules)
- Remove insert_ua_rules and insert_country_rules functions
- reload_dicts now only reloads dict_anubis_ip + dict_anubis_asn
- Simplify CASE blocks in 04_mv_http_logs.sql, 07_ai_features_view.sql,
  view_ai_features_anubis.sql, mv_http_logs.sql: IP > ASN (was 5-level
  UA+IP > UA > IP > ASN > Country cascade)
- Remove dict_anubis_country + dict_anubis_ua from 03_anubis_tables.sql
  (UA table kept as stub for REGEXP_TREE catch-all compatibility)
- Remove anubis_country_rules table from schema
- Remove Anubis UA and Country tabs from dashboard reflists page
- Remove anubis_ua_rules/country_rules from API reflist queries
- deploy_schema.sql simplified from 339 to 122 lines
- 764 lines removed across 9 files

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 15:25:33 +02:00
98abbc80c7 feat(dashboard): page Listes de référence — visualisation CSV/dictionnaires
Nouvelle page /reflists pour visualiser les 9 dictionnaires ClickHouse :
- bot_ip (3.5K entrées) : IP/CIDR de bots connus
- bot_ja4 (31) : fingerprints JA4 de bots
- browser_ja4 (1.2K) : fingerprints JA4 navigateurs → famille, lib TLS
- asn_reputation (82.5K) : ASN → réputation (isp, datacenter, cdn…)
- iplocate_asn (714K) : géolocalisation IP → ASN, pays, nom
- anubis_ua_rules, anubis_ip_rules, anubis_asn_rules, anubis_country_rules

Fonctionnalités :
- 9 onglets de navigation entre les listes
- Recherche textuelle avec filtrage côté ClickHouse
- Pagination (200 entrées/page)
- Tri par colonne (ASC/DESC)
- Graphique de répartition (ECharts) par catégorie
- KPIs dictionnaires en haut de page
- Infobulles de documentation

API : /api/dictionaries, /api/reflist/{name}, /api/reflist/{name}/stats
Helpers : esc() (HTML escape) ajouté à base.html

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 14:56:54 +02:00
039086a0b3 feat: nouvelles techniques de détection et page tactiques SOC
SQL:
- Ajout 5 colonnes d'agrégation (count_xff, count_unusual_ct,
  count_non_std_port, count_login_post, sec_ch_mobile_mismatch)
- Exposition de 5 features calculées dans view_ai_features_1h
- Migration ALTER TABLE pour déploiements existants

Bot-detector:
- 7 nouvelles features ML (has_xff, unusual_content_type_ratio,
  non_standard_port_ratio, login_post_concentration,
  sec_ch_mobile_mismatch, true_window_size, window_mss_ratio)
- Propagation campaign_id vers ml_all_scores (était toujours -1)
- Escalade campagne : HIGH→CRITICAL si cluster ≥5 membres

Dashboard:
- Page Tactiques SOC : brute-force, rotation JA4, récurrence,
  alertes temps réel — 4 KPIs + 4 panneaux + infobulles doc
- Ajout fmtDate() helper global
- Navigation sidebar mise à jour

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 14:29:18 +02:00
702c0d5edb feat(dashboard): add JA4 fingerprint and cluster investigation pages
- /ja4/{fingerprint} page: 8 KPIs, timeline, threat pie, IP scores
  table, ASN/geo charts, HTTP logs, AI features — full JA4 investigation
- /cluster/{cid} page: 8 KPIs, timeline, threat/JA4/ASN/host charts,
  member table with bulk classify — full campaign investigation
- /api/ja4/{fingerprint} and /api/cluster/{cid} API endpoints
- fmtJA4 links now navigate to /ja4/ investigation page
- campaigns.html: 'Ouvrir' button links to /cluster/{cid} full page
- Fix: double-brace {{param}} in non-f-string queries → single {param}
  (was causing HTTP 500 on all parameterized ClickHouse queries)
- 50 routes total, all tests pass, 0 JS console errors

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 14:05:52 +02:00
70188b508c fix(dashboard): eliminate @apply CSS, fix status column, fix click propagation
Playwright testing revealed 3 critical bugs:

1. Tailwind CDN @apply with custom brand-* colors produces empty CSS
   rules, breaking ALL design components (kpi-card, data-table, badges,
   filter-btn, section-card, nav-item). Fix: replace all @apply
   directives with equivalent raw CSS values.

2. Traffic API and IP detail API reference non-existent 'status' column
   in http_logs table → HTTP 500 on /traffic and /ip/{ip}. Fix: remove
   status from SELECT, sort whitelist, filters, and templates.

3. Nested <a> links (fmtJA4, fmtASN, fmtCountry, fmtBotName) inside
   clickable <tr onclick> capture clicks, preventing row navigation to
   /ip/ detail. Fix: add event.stopPropagation() to all formatter links.

Verified with Playwright: 10 pages × 0 JS errors, all tooltips hidden
by default, sidebar toggle works, keyboard shortcuts (Alt+1-9, Alt+B),
classification form saves to DB, campaign detail panel opens on click.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 13:54:38 +02:00
6babc55e3e fix(dashboard): hover infobulles, full-width layout, UX polish
- Fix doc tooltips: split CSS into <style type='text/tailwindcss'> for
  @apply directives + raw CSS for reliable doc panel rendering
- Convert doc panels from click-toggle to hover-based infobulles with
  arrow pointer, fade-in animation, and auto-dismiss on mobile
- Replace '?' icons with 'ⓘ' across all 11 templates (51 tooltips)
- Full-width layout: reduce padding on mobile (px-3), scale up on
  desktop (lg:px-5, xl:px-6) for maximum screen utilization
- Auto-collapse sidebar on narrow screens (<1024px)
- Keyboard shortcuts: Alt+1–9 for page navigation, Alt+B toggle sidebar
- Add LEGITIMATE_BROWSER filter button to detections page
- Sticky header with stronger blur (backdrop-blur-md)
- All 46 routes pass tests

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 13:30:16 +02:00
63ba6d203c feat(dashboard): complete SOC dashboard with full monitoring and workflows
- models.html: Full rewrite — 6 KPIs, scoring volume timeline, anomaly rate
  chart, threat breakdown per model, enhanced model cards with validation gate
- classify.html: SOC workflow — suggested unclassified IPs, quick-classify
  buttons, classification stats pie, pre-fill from URL params
- traffic.html: Clickable rows → ip_detail, column sorting, status column,
  search filter, doc tooltips on all chart sections
- scores.html: Search input, clickable rows → ip_detail, LEGITIMATE_BROWSER
  filter button, doc tooltips on distribution + scatter charts
- ip_detail.html: Resource cascade section (headless browser detection),
  status column in HTTP logs table
- detections.html: Doc tooltips on threat/reason/ASN chart sections
- features.html: Doc tooltips on radar/importance/scatter sections
- api.py: 4 new endpoints — /api/models/timeline, /api/models/threats,
  /api/classify/stats, /api/classify/suggested. Traffic API: status + search.

46 routes total. All tests pass (dashboard + bot-detector 36/36).

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 01:25:01 +02:00
396baa90d2 feat(dashboard): visualisation clusters HDBSCAN
- Page /campaigns dédiée avec 4 vues graphiques :
  · Scatter plot (score vs vélocité, bulles colorées par campagne)
  · Graphe réseau force-directed (IPs liées par JA4 partagé)
  · Grille de cartes campagne (KPIs, ASN, pays, JA4)
  · Panneau détail (radar comportemental, timeline horaire, table membres)
- 4 nouveaux endpoints API :
  · GET /api/campaigns (fix: campaign_id >= 0 au lieu de != '')
  · GET /api/campaigns/graph (nœuds + arêtes)
  · GET /api/campaigns/scatter (score/vélocité par IP)
  · GET /api/campaigns/{cid} (détail + profil + timeline)
- Sidebar: lien Campagnes ajouté dans Surveillance
- Overview: campagnes clickables → lien vers /campaigns

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 01:11:16 +02:00
f1547423b5 refactor(bot-detector): suppression monolithe, tests multifactoriels
- Suppression de bot_detector.py (1982 lignes) remplacé par 11 modules
- Tests navigateur mis à jour pour le système multifactoriel (browser_confidence)
- 36/36 tests passent avec la nouvelle structure modulaire

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 01:03:17 +02:00
1f103392ac refactor(bot-detector): extract monolith into modular package
Split bot_detector.py (~1982 lines) into 10 focused modules:
- config.py: all configuration constants and optional imports
- log.py: logging utilities (log_info, log_decision, append_training_history)
- infra.py: ClickHouse client, health check HTTP server, shutdown
- browser.py: multifactorial browser identification (5 axes)
- scoring.py: drift detection, feature validation, SHAP, clustering
- models.py: EIF, Autoencoder, XGBoost model management
- preprocessing.py: data preprocessing and feature list definitions
- pipeline.py: core semi-supervised scoring loop
- cycle.py: main analysis cycle orchestration
- __main__.py: entry point with startup banner

Update Dockerfile to copy package directory and use python -m bot_detector.

All 36 existing tests pass unchanged.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 01:02:04 +02:00
2d04288e95 feat(dashboard): SOC workflow overhaul — sidebar nav, doc tooltips, full-width layout
- base.html: collapsible sidebar navigation, doc tooltip system, JS helpers
  (fmtNum, fmtPct, fmtDuration, ecGrid, buildTable, docHTML)
- overview.html: SOC command center with stacked timeline, live alerts,
  campaigns panel, browser donut, 6 KPIs
- detections.html: threat color dots, raw score column, click-to-navigate rows
- network.html: JA4 rotation, brute-force, persistent threats tables, 6 KPIs
- ip_detail.html: ASN/country KPIs, AE/XGB/campaign columns, enriched features
- scores/traffic/features/models/classify: page_title blocks + doc tooltips
- api.py: 9 new endpoints (campaigns, brute-force, ja4-rotation, recurrence,
  cascade, alerts, timeline-detail, ua-rotation)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 00:29:34 +02:00
c994ad4466 fix: XGB label query + SHAP isotree compatibility
XGB: query was selecting features from ml_all_scores which doesn't
store them. Now joins ml_all_scores (labels) with view_ai_features_1h
(features). Dynamically discovers available columns to skip thesis §5
features not present in the view. Returns (model, features) tuple.

SHAP: TreeExplainer doesn't support isotree. Fall back to permutation-
based Explainer(model.decision_function, X_sample) for isotree.

Verified: XGB trained on 50000 labels (18436 positives), triple-voice
ensemble scoring active (EIF+AE+XGB), SHAP silent.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-09 00:06:54 +02:00
c6666e2bba fix: isotree score convention — proper sklearn calibration
isotree decision_function returns [0,1] (higher=anomalous, 0.5=boundary).
The entire pipeline (normalize_scores, score_to_threat_level,
compute_adaptive_threshold) expects sklearn convention (negative=anomalous).

Previous fix (-raw_scores) negated all values, making everything
below -0.30 → all CRITICAL. New fix: 0.5 - isotree_score maps
correctly to sklearn's convention:
  isotree 0.80 → -0.30 (CRITICAL)
  isotree 0.65 → -0.15 (HIGH)
  isotree 0.55 → -0.05 (MEDIUM)
  isotree 0.50 →  0.00 (boundary)

Verified: 27,952 LEGITIMATE_BROWSER + 15,843 HIGH + 15,059 MEDIUM
Tests: 36/36 pass.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 23:56:05 +02:00
db306fb9da fix: P0 audit bugs — bot-detector + dashboard + SQL
Bot-detector:
- B1.1: campaign_id and raw_anomaly_score now inserted into ml_detected_anomalies
- B1.4/B1.5: log_decision argument order fixed (cycle_id, name)
- B1.7: AE broadcast error — model now returns features list, scoring
  uses model's features instead of current cycle's (prevents dim mismatch)
- B1.8: Anubis ALLOW bots now get bot_name from anubis_bot_name

Dashboard:
- C1.1: XSS in ip_detail.html — {{ ip | tojson }} instead of raw string
- C1.2: Stored XSS via innerHTML — added escapeHtml() helper, all user-facing
  formatters (fmtIP, fmtASN, fmtCountry, fmtJA4, fmtBotName, fmtLabel) sanitized
- C2.1: status filter now correctly filters http_version column
- C2.2: heatmap toDayOfWeek() - 1 for 0-indexed JS days

SQL:
- B1.3: view_ip_recurrence worst_score uses max() not min() (0=normal, 1=anomal)
- B1.6: view_resource_cascade_1h joined into view_thesis_features_1h (§5.4)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 23:33:00 +02:00
98289ccf04 fix: ASN dictionary pipeline + verbose bot-detector logging
- Fix dict_iplocate_asn: remove non-existent org/domain columns (4→4 cols)
- Add CSV header to iplocate-ip-to-asn.csv (CSVWithNames format)
- Replace org/domain dictGet calls with empty string literals in MV
- Full 714K CIDR stub for complete ASN resolution in tests
- Add header generation to generate_asn_data.py
- Verbose bot-detector stdout: data summary, triage breakdown, model
  training details, scoring stats, browser classification, boxed results
- Fix IPv6 filter in traffic seeder (_ips_from_cidrs)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 17:43:55 +02:00
5c5bca71d1 feat: rewrite ASN classification with PeeringDB + expanded heuristics
Major improvements to generate_asn_data.py:
- Add PeeringDB network data source (34K networks with info_type)
- Add new categories: education, government, enterprise
- Rename 'human' label to 'isp' across all consumers
- Expand keyword heuristics (ISP, datacenter, hosting, CDN, education, gov)
- Add hard-coded lists for education, government, enterprise ASNs
- Support both --output-dir and --output-asn/--output-ipasn CLI interfaces
- Add --no-peeringdb flag for offline use

Results: unknown dropped from 86% to 57%, ISP coverage 21.8K ASNs,
education 3.1K, enterprise 5.7K, government 520.

Updated consumers:
- bot_detector.py: 'human' -> 'isp' for baseline selection
- dashboard api.py: 'human' -> 'isp' in SQL queries
- run-tests.sh: 'human' -> 'isp' in integration test assertions
- update-csv-data.sh: updated label description comment

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 16:02:07 +02:00
9a48fb9d29 feat: LEGITIMATE_BROWSER classification from JA4 + behavioral consistency
Add browser legitimacy classification (A9) to the bot detection pipeline:

- New features: is_known_browser (binary) and browser_consistency_score [0..5]
  combining 5 signals: JA4 browser match, modern_browser_score, Accept-Language,
  cookies, Sec-Fetch-* presence
- Post-scoring: sessions with known browser JA4 + consistency >= 4/5 + NORMAL/LOW
  threat level are reclassified as LEGITIMATE_BROWSER
- Spoofing detection: inconsistent behavior (known JA4 but low consistency) stays
  in normal anomaly scoring — prevents evasion via JA4 spoofing
- XGBoost treats LEGITIMATE_BROWSER as non-threat (negative label)
- ClickHouse: browser_family column added to ml_detected_anomalies and ml_all_scores
- Dashboard: browser_family filter/sort on detections and scores endpoints,
  legitimate_browsers count and browser_stats in overview
- 6 new unit tests covering classification threshold, spoofing, exclusion logic

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 15:46:22 +02:00
7d09c614c3 feat: browser JA4 detection, Anubis bot rules, worldwide ASN data
- Add generate_browser_ja4.py: 1,186 browser JA4 fingerprints from FoxIO + ja4db.com
  covering 11 families (Chromium, Firefox, Safari, Edge, Tor, Opera, Vivaldi...)
- Rewrite generate_bot_ip.py: Anubis YAML rules (Google, Bing, Apple, DuckDuck,
  OpenAI, Perplexity bots) + Tor exit nodes + cloud scanner IPs (3,555 entries)
- Rewrite generate_asn_data.py: worldwide iptoasn.com data (78,049 ASNs, 714K CIDRs)
- Add dict_browser_ja4 ClickHouse dictionary + browser_family in AI features views
- Add /api/browsers dashboard endpoint
- Fix CSV quoting for fields containing commas (User-Agent strings)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 15:27:37 +02:00
b6184e6529 feat: CSV generation scripts, API filter params, enriched CSV stubs
- scripts/generate_bot_ip.py: download Tor exit nodes + curate scanner IPs (1353 entries)
- scripts/generate_bot_ja4.py: 31 bot JA4 fingerprints across 16 families
- scripts/generate_asn_data.py: 38 ASNs + 96 IP-to-ASN prefixes
- scripts/update-csv-data.sh: master orchestrator with --install-stubs
- api.py: add asn_org/country_code/ja4/bot_name filters on detections+scores
- pages.py: add /network route
- csv-stubs: enriched with generated data (Tor nodes, scanner IPs, etc.)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 15:05:43 +02:00
c6ca352db9 feat(dashboard): add clickable drill-down to all data elements
Add navigation helpers (fmtASN, fmtCountry, fmtJA4, fmtBotName,
fmtThreatLink, fmtLabel) to base.html for SOC analyst drill-down.

Update all templates:
- overview.html: clickable table cells + ECharts click handlers for
  ASN, country, JA4, bot, and threat charts
- detections.html: URL param pre-filters, active filter bar with
  clear buttons, clickable ASN/country/JA4/threat in table
- scores.html: URL param pre-filters, clickable threat/JA4/country
- traffic.html: clickable JA4 and country columns
- ip_detail.html: clickable threat/JA4 in detections, clickable
  asn_org/country_code/asn_label in AI features grid
- network.html: click handlers on ASN treemap and country sunburst,
  fmtJA4Full/fmtLabel/fmtBotName/fmtASN in tables
- features.html: scatter plot click navigates to /ip/{ip}

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 14:58:48 +02:00
f448dcb4b0 fix(rpm): standardize systemd scriptlets and unit installation paths
- Add BuildRequires: systemd-rpm-macros to sentinel and correlator specs
- Replace manual systemctl calls with %systemd_post, %systemd_preun,
  %systemd_postun_with_restart macros (handles daemon-reload, stop/disable,
  try-restart on upgrade correctly and is a no-op in containers)
- ja4sentinel.spec: use %{_unitdir} macro instead of hardcoded path
  (/usr/lib/systemd/system); remove cross-service /var/run/logcorrelator
  from %files and %post (owned by logcorrelator package, not sentinel)
- logcorrelator.spec: move unit from /etc/systemd/system (admin namespace)
  to %{_unitdir} (/usr/lib/systemd/system) — correct packaging location;
  move user/group creation from %post to %pre so file ownership is valid
  during RPM install phase; add Requires(pre): shadow-utils; fix bare
  directory entries in %files with %dir macro; add version fallback macro
  so spec is buildable without --define version
- test-rpm.sh: auto-build RPM via Dockerfile.package if dist/rpm/ is
  empty; update service file path check to /usr/lib/systemd/system/

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 10:49:21 +02:00
f7ee5e63f8 fix(docker): add g++ for isotree build, add dashboard Dockerfile.tests
- bot-detector Dockerfile + Dockerfile.tests: install g++ for isotree C++ extension
- dashboard Dockerfile.tests: new smoke test (verify FastAPI app loads)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 08:08:13 +02:00
b735bab5a5 feat(dashboard): rebuild SOC dashboard + fix ClickHouse SQL
Complete rewrite of the SOC dashboard using FastAPI + Jinja2 + htmx + Chart.js + Tailwind CSS.
Replaces the old React/Vite frontend with server-rendered templates.

Dashboard pages:
- Overview: KPIs, timeline chart, threat distribution, top IPs
- Detections: paginated/filterable anomaly table
- Scores: ml_all_scores with AE error & XGB prob columns
- Traffic: HTTP logs with method/host filters
- IP Investigation: full deep-dive (scores, features, HTTP logs, classify)
- Classification: SOC feedback form + history
- Features: AI + thesis feature stats
- Models: scoring stats + model metadata

API: 9 JSON endpoints with parameterized queries, sort whitelists

SQL fixes:
- 05_aggregation_tables: add deduplicate_merge_projection_mode
- 11_views: fix nested aggregate (argMax inside sum)
- 12_thesis_features: remove invalid 'let' bindings, fix groupArrayIf type

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 03:21:05 +02:00
8d58f2b932 feat(bot-detector): add XGBoost supervised third voice (#10)
Triple-voice ensemble architecture:
- EIF (non-supervisé, anomalies zero-day)
- Autoencoder (non-supervisé, corrélations non-linéaires)
- XGBoost (supervisé, patterns connus + feedback SOC)

XGBoost implementation:
- Trained on historical ml_all_scores labels (NORMAL=0, HIGH/CRITICAL/DENY/KNOWN=1)
- Weekly retraining (XGB_RETRAIN_INTERVAL_H=168), min 100 labels required
- Score = predict_proba, combined via meta-learner: (1-β)*(EIF+AE) + β*xgb_prob
- Configurable: XGB_WEIGHT (β=0.20), XGB_MIN_LABELS, XGB_RETRAIN_INTERVAL_HOURS
- Graceful fallback: if xgboost unavailable or labels insufficient, EIF+AE only
- ClickHouse: xgb_prob column added to ml_all_scores
- Tests: 4 new tests (availability, train/predict, meta-learner, save/load)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 02:45:57 +02:00
57cf6c3828 feat(bot-detector): add parallel Autoencoder scorer (#9)
- TrafficAutoEncoder class: symmetric AE (n→64→32→16→32→64→n) with BatchNorm+ReLU
- Trained alongside EIF on human_baseline, saved/loaded with model versioning
- Score = per-sample MSE reconstruction error, combined with EIF via AE_WEIGHT (α=0.30)
- AE latent space (16-dim) used for HDBSCAN clustering instead of raw features
- Configurable: AE_WEIGHT, AE_EPOCHS, AE_LATENT_DIM, AE_LEARNING_RATE
- Graceful fallback: if torch unavailable or AE fails, EIF-only scoring continues
- ClickHouse: ae_recon_error column added to ml_all_scores
- Tests: 5 new tests (AE train/score, encode latent, state dict save/load, weight combination)

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-04-08 02:40:39 +02:00