f1547423b5
refactor(bot-detector): suppression monolithe, tests multifactoriels
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- 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
9a48fb9d29
feat: LEGITIMATE_BROWSER classification from JA4 + behavioral consistency
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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
8d58f2b932
feat(bot-detector): add XGBoost supervised third voice ( #10 )
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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 )
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- 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
f6e2d3c0ca
feat(bot-detector): implement 8 state-of-art improvements
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- EIF: Extended Isolation Forest via isotree (fallback to sklearn IF)
- Benford's Law deviation feature on inter-request timing
- Lag-1 autocorrelation feature for cadence analysis
- Validation gate: reject model if val_anomaly_rate > 20%
- Feature pruning: remove variance < 1e-6 features before training
- Quantile drift: replace N(μ,σ) synthetic with quantile interpolation
- Thread safety: Lock for _service_healthy/_consecutive_failures
- Score normalization: inverted to [0,1] where 1=most anomalous
SQL: add lag1_autocorrelation + benford_deviation to view_thesis_features_1h
Tests: 10 new test functions covering all improvements
Integration: verify_mvs.py checks new thesis feature columns
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com >
2026-04-08 02:31:26 +02:00
9f3e0621e5
feat: split ClickHouse into dual configurable databases (ja4_logs / ja4_processing)
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Architecture:
- ja4_logs: raw log ingestion (http_logs_raw, http_logs, mv_http_logs)
- ja4_processing: analytics, aggregation, ML, dictionaries, audit
Configuration (env vars):
- CLICKHOUSE_DB_LOGS (default: ja4_logs)
- CLICKHOUSE_DB_PROCESSING (default: ja4_processing)
Changes:
- SQL migrations (10 files): all mabase_prod refs → ja4_logs or ja4_processing
with correct cross-database references (MVs, views, dicts)
- deploy_schema.sh: substitutes DB names from env vars at deploy time
- Python shared settings: added CLICKHOUSE_DB_LOGS + CLICKHOUSE_DB_PROCESSING
- Dashboard routes (19 files): replaced ~80 hardcoded mabase_prod refs
with settings.CLICKHOUSE_DB_LOGS / settings.CLICKHOUSE_DB_PROCESSING
- Bot-detector: DB → CLICKHOUSE_DB_PROCESSING, fetch_rules.py configurable
- Correlator: DSN example updated to ja4_logs
- Docker-compose + .env files: new env vars with defaults
- All documentation updated (14 markdown files)
All tests pass: sentinel 10/10, correlator 67.1%, bot-detector 11, dashboard 20, ja4_common 18
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com >
2026-04-07 19:10:35 +02:00
d469e39da7
feat: ja4-platform monorepo — 5 services unified, tests & RPM builds standardized
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Services:
- ja4sentinel: TLS/JA4 fingerprint capture daemon (Go, libpcap)
- logcorrelator: JA4 log correlation engine (Go, ClickHouse)
- mod_reqin_log: Apache module (C, JSON request logging)
- bot_detector: ML bot detection pipeline (Python)
- dashboard: FastAPI/Streamlit analytics UI (Python)
Shared libraries:
- shared/go/ja4common: logger, config, shutdown, ipfilter (Go module)
- shared/python/ja4_common: ClickHouseClient, ClickHouseSettings (Python package)
- shared/clickhouse/: canonical SQL migrations (10 files)
Build & packaging:
- Unified 3-stage Dockerfile.package for Go RPMs (el8/el9/el10)
- go.work workspace linking sentinel, correlator, ja4common
- Makefile with test-all, build-all, rpm-* targets
Fixes applied:
- go.work: 1.21 → 1.24.6 (required by sentinel)
- correlator Dockerfiles: golang:1.21 → golang:1.24
- replace directives in go.mod for ja4common local path
- pyproject.toml: setuptools.backends → setuptools.build_meta
- Removed static libpcap linking (unavailable on Rocky 9)
- Fixed data races in output/writers_test.go (sync.Mutex + atomic.Int32)
- Rewrote corrupted test files (logger_test.go × 2)
Test coverage:
- correlator: 67.1% total (unixsocket 80.5%, config 91.7%, app 83.3%, multi 87.7%, stdout 100%)
- sentinel: all 10 packages pass (api, capture, config, fingerprint, ipfilter, logging, output, tlsparse)
Documentation:
- README.md + docs/ (architecture, development, 5 services, shared libs, DB schema & migrations)
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com >
2026-04-07 16:42:59 +02:00
