# Copilot Instructions — ja4-platform

## What is this?

A monorepo for a JA4/JA3 TLS fingerprinting security pipeline. Five services capture network traffic, correlate logs, detect bots via ML, and present results in a SOC dashboard. All backed by ClickHouse.

**Data flow:** `mod-reqin-log` (Apache HTTP logs) → unix socket → `correlator` ← unix socket ← `sentinel` (TLS/TCP capture) → ClickHouse → `bot-detector` (ML scoring) → `dashboard` (FastAPI SOC UI)

## Build, test, lint

All builds run in Docker — no native Go/Python/C toolchain required on the host.

```sh
# Full suite
make test-all        # run all tests (Docker)
make build-all       # build all service images
make rpm-all         # build RPMs (sentinel, correlator, mod-reqin-log) for el8/el9/el10

# Per-service tests
make test-sentinel          # Go tests (needs --cap-add=NET_RAW inside)
make test-correlator        # Go tests with 80% coverage gate
make test-bot-detector      # Python pytest
make test-dashboard         # Python pytest
make test-ja4common-python  # Python pytest (shared lib)
make test-mod-reqin-log     # C cmocka tests

# Single Go test (from service dir, or via Docker):
docker run --rm -v $(pwd):/build -w /build/services/correlator golang:1.24 \
  go test -v -run TestConfigLoad ./internal/config/

# Single Python test (bot-detector, from repo root):
docker build -f services/bot-detector/bot_detector/Dockerfile.tests -t bd-tests .
docker run --rm bd-tests pytest bot_detector/tests/test_detector.py -v -k test_ae_class_train_and_score

# Local Python tests (needs deps installed):
cd services/bot-detector && pip install -r bot_detector/requirements.txt pytest pytest-mock
pytest bot_detector/tests/test_detector.py -v -k "test_benford"

# Faster correlator build (skip tests):
docker build --target builder --build-arg SKIP_TESTS=true -f services/correlator/Dockerfile .

# Linting (Go only — no Python linter configured)
cd services/sentinel && go vet ./... && gofmt -l .
cd services/correlator && go vet ./... && gofmt -l .

# Full-stack integration tests (Docker Compose, resets DB each run)
make test-integration          # 8 phases: build → start → schema → traffic → pipeline → dashboard → bot-detector → sentinel
make test-integration-keep     # same but leaves stack running after
make test-integration-down     # tear down integration stack
# run-tests.sh also accepts: --build-only (build images without running tests)

# Database management
make init-stack             # initialize ClickHouse schema from scratch
make import-prod-data       # import prod data with date shifting
make purge-db               # DROP and recreate ja4_logs + ja4_processing
make help                   # list all 24+ targets with descriptions
```

## Architecture

### Go workspace (`go.work`, Go 1.24.6)

Three modules in the workspace:
- `services/sentinel` — TLS/TCP packet capture daemon (gopacket/pcap, systemd)
- `services/correlator` — log correlation engine, hexagonal architecture
- `shared/go/ja4common` — shared logger, config, shutdown, ipfilter

Both services have a `replace` directive in their `go.mod` pointing to `../../shared/go/ja4common`. The workspace takes precedence for local dev; the `replace` is needed for Docker builds.

### Correlator hexagonal architecture

```
ports/source.go        → EventSource, CorrelatedLogSink, CorrelationProcessor interfaces
adapters/inbound/      → unixsocket (reads from sentinel + mod-reqin-log)
adapters/outbound/     → clickhouse, file, stdout, multi (fan-out wrapper)
domain/                → CorrelationService, CorrelatedLog, NormalizedEvent
app/                   → Orchestrator (wires everything together)
config/                → YAML config loader
```

### Python services

- `bot-detector` — **Triple-voice ensemble** ML pipeline. Modular package (10 modules):
  - `config.py` → `log.py` → `infra.py` → `browser.py` → `scoring.py` → `models.py` → `preprocessing.py` → `pipeline.py` → `cycle.py` → `__main__.py`
  - Runs two parallel models per cycle:
  - `Complet` (~45 features L3→L7) on correlated traffic (`correlated=1`, TCP+TLS+HTTP)
  - `Applicatif` (~35 features L7-only) on uncorrelated traffic (`correlated=0`)
  - Optional 24h variants when `ENABLE_MULTIWINDOW=true`
  
  **ML ensemble:**
  - **Extended Isolation Forest** (isotree, primary unsupervised scorer, falls back to sklearn)
  - **Autoencoder** (PyTorch, architecture n→64→32→16→32→64→n, reconstruction error scorer)
  - **XGBoost** (supervised, trained on SOC analyst labels from `soc_feedback`)
  - Score: `final = (1-XGB_WEIGHT) * ((1-AE_WEIGHT)*eif_norm + AE_WEIGHT*ae_norm) + XGB_WEIGHT*xgb_prob`
  - Default weights: `AE_WEIGHT=0.30`, `XGB_WEIGHT=0.20`
  
  Also: adaptive thresholds, SHAP explainability, HDBSCAN campaign clustering, Benford deviation, lag-1 autocorrelation, quantile drift detection, feature pruning, validation gate.
  
  `model_name` is part of the ORDER BY key in both `ml_detected_anomalies` and `ml_all_scores`. Uses `os.getenv()` directly for config, NOT pydantic-settings.

- `dashboard` — FastAPI + Jinja2 server-side templates + htmx + Chart.js + Tailwind CSS (CDN). Two route modules: `backend/routes/api.py` (9 JSON endpoints) and `backend/routes/pages.py` (8 HTML pages). Uses `os.getenv()` with `safe_identifier()` for DB name validation.

- `shared/python/ja4_common` — `ClickHouseClient` singleton + `ClickHouseSettings` (pydantic-settings). Installed as a local package in each Python Dockerfile. **Note:** dashboard does NOT use ja4_common — it has its own lightweight `clickhouse-connect` client in `backend/database.py`.

### C module

- `mod-reqin-log` — Apache HTTPD module (C11, built with `apxs`). Logs HTTP requests as JSON to a Unix socket. Tests use cmocka.

## ClickHouse dual-database pattern

Two configurable databases (env vars with defaults):

| Env var | Default | Contains |
|---------|---------|----------|
| `CLICKHOUSE_DB_LOGS` | `ja4_logs` | `http_logs_raw`, `http_logs`, `mv_http_logs` |
| `CLICKHOUSE_DB_PROCESSING` | `ja4_processing` | Aggregations, ML tables, views, dicts, audit |

**Cross-database references exist** — materialized views in one DB read from the other:
- `ja4_logs.mv_http_logs` references `ja4_processing.dict_anubis_*` and `ja4_processing.dict_iplocate_asn`
- `ja4_processing.mv_agg_*` reads `FROM ja4_logs.http_logs`

**In Python code**, always use fully qualified table names:
```python
from ..config import settings
query = f"SELECT ... FROM {settings.CLICKHOUSE_DB_PROCESSING}.ml_detected_anomalies ..."
query = f"SELECT ... FROM {settings.CLICKHOUSE_DB_LOGS}.http_logs ..."
```
Never hardcode database names in queries.

**In Go (correlator)**, the database is part of the ClickHouse DSN (`clickhouse://user:pass@host:9000/ja4_logs`). The target table is configurable via YAML (`outputs.clickhouse.table`).

**SQL schema** has two deployment paths:
- **Base schema**: `shared/clickhouse/` (13 ordered files, `00_database.sql` through `12_thesis_features.sql`). Deploy with `shared/clickhouse/deploy_schema.sh` which substitutes DB names from env vars.
- **Post-deploy migrations**: `services/correlator/sql/migrations/` — ALTER TABLE statements for existing deployments. Run manually: `clickhouse-client --multiquery < file.sql`.

### Data retention (TTL)

| Table | TTL | Partition |
|-------|-----|-----------|
| `http_logs_raw` | 2 hours | `toStartOfHour(ingest_time)` |
| `http_logs` | 30 days | `toDate(log_date)` |
| `agg_host_ip_ja4_1h` | 7 days | `toDate(window_start)` |
| `agg_header_fingerprint_1h` | 7 days | `toDate(window_start)` |
| `agg_ip_behavior_1h` | 7 days | `toDate(window_start)` |
| `agg_request_timing_1h` | 7 days | `toDate(window_start)` |
| `agg_path_sequences_1h` | 7 days | `toDate(window_start)` |
| `agg_resource_cascade_1h` | 7 days | `toDate(window_start)` |
| `ml_detected_anomalies` | 7 days | `toDate(detected_at)` |
| `ml_all_scores` | 7 days | `toDate(window_start)` |

All aggregation/ML tables use `ttl_only_drop_parts=1` for efficient partition-level expiry.

## Key conventions

### Docker-first builds
Every service has `Dockerfile` (prod), `Dockerfile.dev` or `Dockerfile.tests` (tests), and Go/C services have `Dockerfile.package` (RPM packaging via 3-stage: builder → rpmbuild × 3 distros → alpine output).

### Go config: YAML + env vars
- Sentinel: `config.yml`, env prefix `JA4SENTINEL_`
- Correlator: `config.yml`, env prefix `LOGCORRELATOR_`
- Both support `SIGHUP` for log rotation

### Python config
- ja4_common: `ClickHouseSettings(BaseSettings)` — pydantic-settings singleton
- bot-detector: raw `os.getenv()` with defaults, NOT pydantic-settings
- dashboard: `backend/config.py` reads `CLICKHOUSE_DB_PROCESSING`/`CLICKHOUSE_DB_LOGS` from env, validates identifiers with `safe_identifier()` regex

### Dashboard route structure
Two route modules: `backend/routes/api.py` (JSON API) and `backend/routes/pages.py` (HTML).
API endpoints use parameterized ClickHouse queries with sort/order whitelists:
```python
from backend.config import DB_PROCESSING, DB_LOGS, safe_identifier
from backend.database import query, query_scalar, execute

_DB = safe_identifier(DB_PROCESSING)
rows = query(
    f"SELECT ... FROM {_DB}.ml_detected_anomalies "
    "WHERE src_ip = toIPv6({ip:String})",
    {"ip": ip_value},
)
```
**IPv4/IPv6 duality**: `http_logs.src_ip` is `IPv4`, but ML tables (`ml_all_scores`, `ml_detected_anomalies`) store `IPv6` (mapped as `::ffff:x.x.x.x`). Use `toIPv6()` for ML table queries, `toIPv4OrZero()` for http_logs queries. Strip `::ffff:` prefix when displaying IPs.

### Browser detection: NO User-Agent
Browser identification uses 5 axes in `browser.py` — **none rely on User-Agent** (trivially spoofable):
- **Axe 1 (0.30)**: JA4 fingerprint match in `dict_browser_ja4` (TLS-level, hardest to fake)
- **Axe 2 (0.15)**: JA4 structural analysis (TLS1.3, h2/h3, cipher/extension counts)
- **Axe 3 (0.20)**: Client Hints (`has_sec_ch_ua`) + Sec-Fetch-* consistency + `is_fake_navigation`
- **Axe 4 (0.15)**: Navigation behavior (cookies, referers, asset ratio)
- **Axe 5 (0.20)**: TLS/TCP coherence (ALPN, window scale, TLS version)

`modern_browser_score` in SQL: sec-ch-ua→100, Sec-Fetch→70, else→0. Never uses UA presence.
`ua_ch_mismatch` and `is_ua_rotating` remain as ML anomaly features (detect bots), but are NOT used for browser identification.

### RPM spec files
Located at `services/<name>/packaging/rpm/<name>.spec`. Version injected via `--define "build_version X.Y.Z"` at build time.

### Inter-service communication
Services communicate via **Unix sockets**, not HTTP:
- `sentinel` → `/var/run/logcorrelator/network.socket` → `correlator` (source B: TLS/TCP data)
- `mod-reqin-log` → `/var/run/logcorrelator/http.socket` → `correlator` (source A: HTTP data)
- `correlator` → ClickHouse (batch inserts into `ja4_logs.http_logs_raw`)

### Sentinel requires elevated privileges
Tests need `--cap-add=NET_RAW --cap-add=NET_ADMIN` for packet capture (pcap).

## Comments standard

All code is commented in **French** (identifiers stay in English). Standard defined in `docs/commenting-standard.md`:
- **Go**: godoc `// FuncName does X`, package-level `// Package foo fournit...`
- **Python**: PEP-257 triple-quoted French docstrings on all functions/classes/modules
- **C**: Doxygen `/** @brief ... @param ... @return ... */` before every function, `/* ====== Section ====== */` banners
- **Bash**: standardized header block with `Usage:` and `Variables d'environnement:`
- **SQL**: `-- === filename.sql — description ===` banner + `-- --- Table ---` section headers

## Known gotchas

### go.work and Docker build contexts
When building either `sentinel` or `correlator` in Docker, the build context must include **both** service directories because `go.work` references them both. The root-level Makefiles always use `.` (repo root) as context — don't change this.

### Correlator YAML does not expand env vars
Go's YAML parser reads `${VAR:-default}` as a **literal string**. Use hardcoded values or pass values directly in the YAML file. This is why `tests/integration/platform/correlator.yml` has a hardcoded DSN.

### Anubis reference lists: IP/CIDR + ASN only
The Anubis pipeline was simplified — only `dict_anubis_ip` (IP/CIDR) and `dict_anubis_asn` exist. UA and Country dictionaries were removed. The `fetch_rules.py` script only imports IP ranges and ASN numbers from Anubis lists.

### TLS/pcap capture needs non-loopback traffic
`sentinel` listens on a network interface (e.g., `eth0`), not loopback. Traffic sent to `localhost` or `127.0.0.1` from the same container is invisible to pcap. In integration tests, traffic must come from a separate container crossing the Docker bridge network.

### ClickHouse initialization timing
ClickHouse takes ~15-20s to initialize all 13 SQL files. Integration health checks use a 120s timeout (not the default 60s).

### ClickHouse 24.8 SQL gotchas
- **Projections in AggregatingMergeTree** require `SETTINGS deduplicate_merge_projection_mode = 'drop'`
- **No `let` bindings** in SQL expressions — inline everything or use CTEs
- **Nested aggregates are illegal**: `argMax(any(col), sum(hits))` fails. Use a subquery to pre-aggregate, then apply the outer aggregate
- **`groupArrayIf` column type** must match the State/Merge functions: if the MV uses `groupArrayIfState()`, the column must be `AggregateFunction(groupArrayIf(...), Type, UInt8)`, not plain `groupArrayState`
- **`SimpleAggregateFunction` columns** are auto-wrapped in their aggregate function during queries on AggregatingMergeTree. So `argMax(ja4, hits)` where `hits` is `SimpleAggregateFunction(sum, UInt64)` becomes `argMax(ja4, sum(hits))` — a nested aggregate error

### Bot-detector test patterns
Tests in `bot_detector/tests/test_detector.py` are **self-contained** — they re-implement key logic rather than importing from the main modules (avoids heavy `torch`/`sklearn`/`isotree` import chains). Autoencoder tests use a local `_make_ae()` helper. XGBoost tests create in-memory models. Tests skip gracefully if `torch` or `xgboost` are not installed (`pytest.skip`).

### RPM builds must use Rocky Linux
All `Dockerfile.package` files use `rockylinux:9` (or `rockylinux:8`/`almalinux:10`) as the build base — never Debian-based images. Reason: Rocky provides `libpcap.so.1`; Debian provides `libpcap.so.0.8`. Building sentinel on Debian and running on Rocky produces a missing library error at runtime.

### ClickHouse `FLAT()` layout requires numeric keys
If adding a new dictionary with a `String` primary key, use `COMPLEX_KEY_HASHED()` not `FLAT()`.