Replace single-service-per-endpoint with all-ips mode running nginx, apache,
and hitch+varnish simultaneously on 3 dedicated IPs per VM (eth1 alias IPs).
Add a dedicated traffic VM with curl-impersonate for realistic TLS fingerprints,
parallelized traffic generation, and paired SNI_HOSTS/TARGET_IPS lists for
per-VM per-service hostname identification (e.g. rocky9-nginx-platform.test).
Key changes:
- run-tests-vm.sh: add setup_all_ips(), IP-specific Listen/bind directives
with reset-before-apply pattern, graceful service availability checks
- run-e2e-test.sh: traffic VM architecture, all-ips mode, eth1 network,
paired IP/SNI lists, updated cleanup for alias IPs
- generate-traffic.sh: parallel background jobs, curl-impersonate detection,
auto source interface detection via ip route get, Host header in HTTP traffic
- Vagrantfile: add traffic VM with provision-traffic.sh
- provision-traffic.sh: install curl-impersonate and httpx for traffic gen
- test-rpm.sh: multi-interface TC check, updated ja4ebpf config
- clickhouse-init.sh: load CSV stubs for Anubis/bot-networks dictionaries
- Remove obsolete correlator/sentinel/mod-reqin-log docs
- Add h2_settings_ack column to http_logs schema
- Upgrade Go toolchain to 1.25.0
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add run-e2e-test.sh with CLI parameters (--hits, --http-ratio, --dns, --tls,
--src-ips, --keep-analysis, --up) for configurable traffic generation. Traffic
runs from VM endpoints with multiple source IPs (alias IPs on eth0) to produce
distinct sessions for the ML pipeline. Fix curl TLS flags (--tlsv1.2 instead
of --tls-v1-2), skip redundant local verification in distributed mode, and
fix dashboard is_available() cache that never retried after ClickHouse recovery.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace the LogisticRegression meta-learner with a PyTorch MetaFusionMLP
(Linear(3,16)->BN->ReLU->Dropout->Linear(16,1)->Sigmoid) for non-linear
fusion of EIF, NF, and XGBoost scores. Replace KS-test + quantile digest
drift detection with ADWIN (adaptive sliding window, Hoeffding bound).
Replace weekly XGBoost batch retraining with River HoeffdingAdaptiveTree
for incremental online learning (learn_one per cycle). Update all thesis
documentation sections (2.4.2c, 2.4.3, 3.8, discussion, conclusion).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Rewrite fleet.py to use a GNN-based approach: nodes are src_ip with ML feature
vectors, edges connect IPs sharing (JA4, ASN) pairs, GraphSAGE (2 SAGEConv
layers, in→64→32) produces 32D embeddings clustered by HDBSCAN. PyG NeighborLoader
activates for >50k nodes. Update thesis docs (§5.2, §6.4, §2, §8) to reflect
GraphSAGE architecture and PyG scalability.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Split monolithic thesis into separate chapter markdown files under
docs/thesis/. Remove fabricated bibliography entries, correct inflated
claims, add GNN/Transformers section, and rename MetaLearner to Fusion LR.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implement offline profile building (profile_builder.py) and real-time
dynamic scoring (browser_matcher_dynamic.py) using HDBSCAN-based browser
fingerprint clustering. Add ClickHouse materialized view (13_h2_profiling.sql)
for h2_profile_stats aggregation. Update thesis and project documentation
to cover the new dynamic profiling architecture.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Dockerfile.package: migre go-builder de golang:bookworm (Debian) vers
rockylinux:9, installe Go depuis le tarball officiel, remplace apt par
dnf (clang llvm libbpf-devel bpftool)
- Suppression du champ 'correlated' de l'agent ja4ebpf : avec eBPF/XDP,
la corrélation L3/L4↔L7 est toujours implicite par présence des champs.
Supprimé de : session.go, manager.go, main.go (x5), clickhouse.go
- Thèse (6 corrections listées + cohérence correlated) :
1. §3.5 + §3.9.1 : SSL_read retourne des octets bruts sans respecter les
frontières H2 → buffer circulaire de réassemblage en Go userspace
2. §3.1 : supprimé libpcap + CAP_NET_RAW, remplacé par définition uprobe
3. §4 + §7 : compte exact 96 features en 8 familles (Famille 1–8),
supprimé taxonomie F1–F11 obsolète, tous les totaux mis à jour
4. §2.4 + §8 : remplacé 7 fausses URLs arXiv par [Référence à vérifier]
5. §4 Famille 2 : ja4_drift_ratio → renvoi à Famille 8 (définition complète)
6. §6.4 : ajouté limite 'Overhead de l'uprobe SSL_read'
+ §3.6 : supprimé correlated=0/1 du texte architectural
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
- Use two separate //go:generate directives (Ja4Tc for tc_capture.c, Ja4Ssl
for uprobe_ssl.c) to avoid duplicate LICENSE symbol and multi-file clang issue
- Update loader.go to hold tcObjs/sslObjs separately with correct field names:
UprobeSslSetFd, UprobeSslReadEntry, UretprobeSslReadExit,
KprobeAccept4Entry, KretprobeAccept4Exit
- Add systemd-rpm-macros to all three RPM build stages (el8/el9/el10)
so that %{_unitdir} macro resolves correctly
- RPMs now build successfully for el8, el9, el10
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
- §2.1.3: Simplifié Anubis à 2 dictionnaires (dict_anubis_ip, dict_anubis_asn) avec priorité COALESCE
- §2.4.2: Ajouté bibliothèque isotree, formule de calibration, ntrees=300, sérialisation joblib
- §2.4.2b/§2.4.4: Remplacé DBSCAN par HDBSCAN partout
- §2.4.2c: Remplacé régression logistique par pondération linéaire fixe, ajouté formule et poids
- §2.4.3: Clarifié approximation par 5 quantiles pour la détection de dérive
- §3.1: Mis à jour le diagramme ASCII (dual-database, 3×EIF+AE+XGB, HDBSCAN, 55 routes)
- §3.8: Mis à jour la trifurcation + ajouté détection multifactorielle navigateur (5 axes)
- §4: Élargi taxonomie de 51 à 65+ features sur 8 familles
- §5: Ajouté statut d'implémentation (✅/❌) à chaque technique
- §6: Ajouté §6.6 résultats de déploiement (3M+ logs, 34K sessions/cycle)
- §7: Mis à jour conclusion (65+ features, 5/8 techniques, refactorisation modulaire)
- §8: Ajouté références isotree, PyTorch, HDBSCAN, XGBoost, SHAP
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Analyse exhaustive feature-par-feature des techniques de détection
implémentées vs ce que décrit la thèse. Score: 97% base, 6% techniques
avancées, 72% global pondéré.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
deploy_views.sql (v13 → v14):
- CRITICAL: ml_detected_anomalies ORDER BY (src_ip) → (src_ip, ja4, host, model_name)
ReplacingMergeTree was collapsing all detections to 1 row per IP on merge
- Add PARTITION BY toDate + ttl_only_drop_parts on all 4 data tables
- ml_all_scores TTL 3d → 7d; ml_detected_anomalies TTL 30d → 7d
- agg_host_ip_ja4_1h + agg_header_fingerprint_1h: add partition + TTL 7d
- view_ip_recurrence: add WHERE detected_at >= now() - 7 DAY (was full scan)
- Remove dead views: summary/timeseries/threat_dist/variability
- Add view_dashboard_entities (fixes HTTP 500 in clustering/incidents/fingerprints)
- Add view_dashboard_user_agents (fixes HTTP 500 in fingerprints/metrics)
- Add view_ai_features_24h (enables ENABLE_MULTIWINDOW in bot_detector)
- Mark max_requests_per_sec as DEPRECATED (always 0)
New files:
- correlator/sql/migrations/01_ttl_adjustments.sql: ALTER TABLE migration
- tests/integration/verify_mvs.py: MV pipeline verification assertions
- docs/THESIS_HTTP_Traffic_Detection.md: detection techniques thesis
All DB references use ja4_processing/ja4_logs (no mabase_prod).
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Couvre les 7 étapes de mise en place :
1. Installation ClickHouse
2. Déploiement du schéma (deploy_schema.sh + migrations)
3. Configuration des utilisateurs (data_writer, analyst, bot_writer)
4. Fichiers CSV externes pour les dictionnaires
5. Installation des services Go via RPM (sentinel, correlator, mod-reqin-log)
6. Installation des services Python via Docker (bot-detector, dashboard)
7. Vérification bout-en-bout (ingestion, agrégation, ML, dashboard)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
