maj sql des vues
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docs/detection.md
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# Architecture de détection — logcorrelator
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## Vue d'ensemble
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Le système de détection est composé de **trois couches** qui s'enchaînent en pipeline :
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```
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Trafic HTTP/TLS capturé
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│
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▼
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┌───────────────────┐
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│ ClickHouse │ Stockage, agrégation, vues heuristiques
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│ (SQL pipeline) │
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└────────┬──────────┘
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│
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▼
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┌───────────────────┐
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│ bot_detector.py │ Modèle IA (Isolation Forest, cycle 5 min)
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│ (Python / ML) │
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└────────┬──────────┘
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│
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▼
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┌───────────────────┐
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│ ml_detected_ │ Table de résultats (ReplacingMergeTree)
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│ anomalies │
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└───────────────────┘
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```
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---
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## 1. Ingestion des logs (`http_logs_raw` → `http_logs`)
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Les logs bruts arrivent en JSON dans la table `http_logs_raw`. Une **vue matérialisée** (`mv_http_logs`) les parse en temps réel et alimente la table `http_logs`, qui contient les champs structurés suivants :
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| Catégorie | Champs clés |
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|---|---|
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| Réseau | `src_ip`, `src_port`, `dst_ip`, `dst_port` |
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| Enrichissement | `src_asn`, `src_country_code`, `src_as_name` (via dictionnaire IPLocate) |
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| HTTP | `method`, `host`, `path`, `query`, `http_version` |
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| Corrélation | `correlated`, `orphan_side`, `conn_id`, `keepalives` |
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| Métadonnées IP | `ip_meta_ttl`, `ip_meta_id`, `ip_meta_df`, `ip_meta_total_length` |
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| Métadonnées TCP | `tcp_meta_window_size`, `tcp_meta_mss`, `tcp_meta_window_scale`, `tcp_meta_options` |
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| TLS / Fingerprint | `tls_version`, `tls_sni`, `tls_alpn`, `ja3`, `ja3_hash`, `ja4` |
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| En-têtes HTTP | `header_user_agent`, `header_sec_ch_ua*`, `header_sec_fetch_*`, … |
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L'enrichissement IP est réalisé via le dictionnaire `dict_iplocate_asn` (fichier CSV chargé en mémoire, rechargé toutes les 1-2 heures).
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---
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## 2. Agrégation comportementale (fenêtre horaire)
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Deux tables d'agrégation `AggregatingMergeTree` sont alimentées en continu par des vues matérialisées.
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### 2.1 `agg_host_ip_ja4_1h` — Comportement réseau & applicatif
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Agrège par triplet **(window_start, src_ip, ja4, host)** toutes les heures :
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| Métrique agrégée | Signification |
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|---|---|
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| `hits` | Nombre total de requêtes |
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| `count_post` | Requêtes POST |
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| `uniq_paths` | Chemins distincts visités |
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| `uniq_query_params` | Paramètres de query distincts |
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| `unique_src_ports` | Ports sources distincts |
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| `unique_conn_id` | Connexions TCP distinctes |
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| `max_keepalives` | Réutilisation maximale d'une connexion |
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| `orphan_count` | Requêtes sans corrélation TCP complète |
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| `ip_id_zero_count` | Paquets avec IP ID = 0 (spoofing potentiel) |
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| `tcp_fp_raw` | Hash de l'empreinte TCP (window, MSS, scale, options) |
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| `tcp_jitter_variance` | Variance du délai SYN→ClientHello (jitter TLS) |
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| `total_ip_length_var` | Variance de la taille des paquets IP |
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| `mss_1460_count` | Requêtes avec MSS = 1460 (signature Ethernet/desktop) |
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### 2.2 `agg_header_fingerprint_1h` — Empreinte des en-têtes HTTP
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Agrège par **(window_start, src_ip)** :
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| Métrique | Signification |
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|---|---|
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| `header_order_hash` | Hash de l'ordre des en-têtes (fingerprint JA4H) |
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| `header_count` | Nombre d'en-têtes distincts |
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| `has_accept_language` | Présence de `Accept-Language` |
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| `has_cookie` | Présence de `Cookie` |
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| `has_referer` | Présence de `Referer` |
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| `modern_browser_score` | Score 0/50/100 selon présence UA et `Sec-CH-UA` |
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| `ua_ch_mismatch` | Incohérence entre `User-Agent` et `Sec-CH-UA-Platform` |
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| `sec_fetch_mode/dest` | Contexte de navigation déclaré |
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---
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## 3. Exclusions (listes blanches)
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Avant toute analyse, deux tables permettent d'**exclure les robots légitimes** connus :
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- `bot_ip` (fichier `bot_ip.csv`) — IPs à ignorer (crawlers, monitoring…)
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- `bot_ja4` (fichier `bot_ja4.csv`) — Fingerprints JA4 à ignorer
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- `ref_bot_networks` — Réseaux CIDR IPv4/IPv6 catégorisés (légitimes ou malveillants)
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Ces exclusions sont appliquées dans la vue `view_ai_features_1h`.
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---
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## 4. Vue IA : `view_ai_features_1h`
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Cette vue consolidée **sur 24 heures glissantes** calcule les **28 features** passées au modèle ML. Elle joint les deux tables d'agrégation et dérive les métriques suivantes :
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| Feature | Calcul | Signal détecté |
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|---|---|---|
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| `hit_velocity` | `hits / durée_en_secondes` | Volume de requêtes anormalement élevé |
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| `fuzzing_index` | `uniq_query_params / uniq_paths` | Exploration paramétrique (fuzzing) |
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| `post_ratio` | `count_post / hits` | Soumission de formulaires en masse |
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| `port_exhaustion_ratio` | `unique_src_ports / hits` | Rotation de ports (scan) |
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| `orphan_ratio` | `orphan_count / hits` | Requêtes sans handshake complet |
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| `ip_id_zero_ratio` | `ip_id_zero_count / hits` | Spoofing d'adresse IP |
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| `multiplexing_efficiency` | `hits / unique_conn_id` | Réutilisation des connexions (H2/H3) |
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| `true_window_size` | `tcp_win * 2^tcp_scale` | Taille réelle de la fenêtre TCP |
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| `window_mss_ratio` | `tcp_win / tcp_mss` | Cohérence TCP stack |
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| `tcp_jitter_variance` | Variance SYN→ClientHello | Irrégularité du timing TLS |
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| `alpn_http_mismatch` | ALPN=h2 mais HTTP/1.1 | Négociation TLS mensongère |
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| `is_alpn_missing` | ALPN absent ou `00` | Client non-standard |
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| `sni_host_mismatch` | SNI ≠ Host header | Proxy transparent / bot |
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| `mss_mobile_mismatch` | MSS=1460 + score navigateur élevé | Client mobile simulé depuis desktop |
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| `is_fake_navigation` | `sec_fetch_mode=navigate` mais `sec_fetch_dest≠document` | Navigation simulée |
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| `tcp_shared_count` | Nb d'IPs partageant la même empreinte TCP | Infrastructure partagée / botnet |
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| `header_order_shared_count` | Nb d'IPs partageant le même ordre d'en-têtes | Outil automatisé commun |
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---
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## 5. Modèle IA : Isolation Forest (`bot_detector.py`)
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### Cycle d'exécution
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Le service tourne en boucle avec un **cycle de 5 minutes** :
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```
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fetch_and_analyze()
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│
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├─ Requête SELECT * FROM view_ai_features_1h
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│
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├─ Nettoyage des données (fillna)
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│
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├─ Dual-Model routing :
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│ ├─ [Complet] correlated=1 → 23 features (réseau + TLS + headers)
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│ └─ [Applicatif] correlated=0 → 19 features (headers + comportement)
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│
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└─ INSERT INTO ml_detected_anomalies
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```
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### Paramétrage du modèle
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| Paramètre | Valeur | Signification |
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|---|---|---|
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| `n_estimators` | 200 | Nombre d'arbres d'isolation |
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| `contamination` | 0.2% | Proportion de bots attendue dans le trafic |
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| `seuil de score` | < -0.05 | Score en dessous duquel une session est marquée anomalie |
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| `volume minimum` | 500 sessions | En dessous, le modèle est ignoré (trop peu de données) |
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### Dual-Model routing
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Le trafic est **séparé en deux populations** selon le champ `correlated` :
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- **Modèle Complet** (`correlated=1`) : la corrélation TCP↔HTTP est disponible → les features réseau (TTL, jitter TLS, ALPN, SNI) sont fiables et ajoutées à l'analyse.
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- **Modèle Applicatif** (`correlated=0`) : seule la couche HTTP est disponible → l'analyse se concentre sur le comportement applicatif (headers, paths, POST ratio…).
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---
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## 6. Vues heuristiques statiques
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En parallèle du modèle IA, cinq vues SQL fournissent des **détections déterministes** sans ML, sur fenêtre 24h :
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| Vue | Règle de détection |
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|---|---|
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| `view_host_ip_ja4_rotation` | IP avec ≥ 5 fingerprints JA4 distincts et > 100 requêtes → rotation d'identité |
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| `view_host_ja4_anomalies` | Fingerprint JA4 vu depuis ≥ 20 IPs sur ≥ 3 hôtes → outil de scan distribué |
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| `view_form_bruteforce_detected` | ≥ 10 query params distincts et ≥ 20 hits → brute-force de formulaire |
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| `view_alpn_mismatch_detected` | HTTP/1.1 avec ALPN h2 ou h3 et ≥ 10 hits → négociation TLS frauduleuse |
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| `view_tcp_spoofing_detected` | TTL ≤ 64 avec User-Agent Windows ou iPhone → empreinte OS incohérente |
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---
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## 7. Résultats : `ml_detected_anomalies`
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Les anomalies détectées sont stockées dans une table `ReplacingMergeTree(detected_at)` avec **TTL 30 jours**. La clé d'ordre `(src_ip, ja4, host)` garantit que chaque triplet ne conserve que la **détection la plus récente** (dédoublonnage automatique).
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Chaque enregistrement contient :
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- Les scores et features ayant conduit à la détection
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- Le champ `reason` : texte lisible avec score, vélocité, et indice de fuzzing
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- Le champ `is_headless` : déduit de l'incohérence `sec_fetch_mode`
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---
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## 8. Schéma de flux complet
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```
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┌─────────────────────────────────────┐
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│ http_logs_raw (JSON) │
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└──────────────┬──────────────────────┘
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│ mv_http_logs (MV)
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▼
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┌─────────────────────────────────────┐
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│ http_logs (parsée) │
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└────────┬──────────────┬─────────────┘
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│ │
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mv_agg_host_ip_ja4 │ │ mv_agg_header_fingerprint
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▼ ▼
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┌──────────────────┐ ┌──────────────────────────┐
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│ agg_host_ip_ja4 │ │ agg_header_fingerprint │
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│ _1h │ │ _1h │
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└────────┬─────────┘ └──────────┬──────────────┘
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│ │
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└──────────┬─────────────┘
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│ view_ai_features_1h (JOIN + calculs)
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▼
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┌─────────────────────────────────────┐
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│ bot_detector.py (Isolation Forest) │
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│ Cycle : 5 min | Fenêtre : 24h │
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└──────────────┬──────────────────────┘
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│
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▼
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┌─────────────────────────────────────┐
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│ ml_detected_anomalies │
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│ (ReplacingMergeTree, TTL 30j) │
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└─────────────────────────────────────┘
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```
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153
sql/views.sql
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sql/views.sql
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<-- 1. NETTOYAGE COMPLET
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DROP TABLE IF EXISTS mabase_prod.ml_detected_anomalies;
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DROP VIEW IF EXISTS mabase_prod.view_ai_features_1h;
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DROP VIEW IF EXISTS mabase_prod.view_host_ip_ja4_rotation;
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DROP VIEW IF EXISTS mabase_prod.view_host_ja4_anomalies;
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DROP VIEW IF EXISTS mabase_prod.view_form_bruteforce_detected;
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DROP VIEW IF EXISTS mabase_prod.view_alpn_mismatch_detected;
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DROP VIEW IF EXISTS mabase_prod.view_tcp_spoofing_detected;
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DROP VIEW IF EXISTS mabase_prod.mv_agg_host_ip_ja4_1h;
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DROP TABLE IF EXISTS mabase_prod.agg_host_ip_ja4_1h;
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DROP VIEW IF EXISTS mabase_prod.mv_agg_header_fingerprint_1h;
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DROP TABLE IF EXISTS mabase_prod.agg_header_fingerprint_1h;
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-- 2. TABLES D'EXCLUSION
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CREATE TABLE IF NOT EXISTS mabase_prod.bot_ip (ip String) ENGINE = File(CSV, 'bot_ip.csv');
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CREATE TABLE IF NOT EXISTS mabase_prod.bot_ja4 (ja4 String) ENGINE = File(CSV, 'bot_ja4.csv');
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-- 3. AGRÉGATION COMPORTEMENTALE (26 DIMENSIONS)
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CREATE TABLE mabase_prod.agg_host_ip_ja4_1h
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(
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window_start DateTime, src_ip String, ja4 String, host String,
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first_seen SimpleAggregateFunction(min, DateTime), last_seen SimpleAggregateFunction(max, DateTime),
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hits SimpleAggregateFunction(sum, UInt64), count_post SimpleAggregateFunction(sum, UInt64),
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uniq_paths AggregateFunction(uniq, String), uniq_query_params AggregateFunction(uniq, String),
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src_country_code SimpleAggregateFunction(any, String), tcp_fp_raw SimpleAggregateFunction(any, String),
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tcp_jitter_variance AggregateFunction(varPop, Float64), tcp_win_raw SimpleAggregateFunction(any, UInt32),
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tcp_scale_raw SimpleAggregateFunction(any, UInt32), tcp_mss_raw SimpleAggregateFunction(any, UInt32),
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tcp_ttl_raw SimpleAggregateFunction(any, UInt32), http_ver_raw SimpleAggregateFunction(any, String),
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tls_alpn_raw SimpleAggregateFunction(any, String), tls_sni_raw SimpleAggregateFunction(any, String),
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first_ua SimpleAggregateFunction(any, String), correlated_raw SimpleAggregateFunction(max, UInt8),
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unique_src_ports AggregateFunction(uniq, UInt16), unique_conn_id AggregateFunction(uniq, String),
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max_keepalives SimpleAggregateFunction(max, UInt32), orphan_count SimpleAggregateFunction(sum, UInt64),
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ip_id_zero_count SimpleAggregateFunction(sum, UInt64), total_ip_length_var AggregateFunction(varPop, Float64),
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mss_1460_count SimpleAggregateFunction(sum, UInt64)
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) ENGINE = AggregatingMergeTree() ORDER BY (window_start, src_ip, ja4, host);
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CREATE MATERIALIZED VIEW mabase_prod.mv_agg_host_ip_ja4_1h TO mabase_prod.agg_host_ip_ja4_1h AS
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SELECT
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toStartOfHour(src.time) AS window_start, src.src_ip, src.ja4, src.host,
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min(src.time) AS first_seen, max(src.time) AS last_seen, count() AS hits,
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sum(IF(src.method = 'POST', 1, 0)) AS count_post, uniqState(src.path) AS uniq_paths,
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uniqState(src.query) AS uniq_query_params, any(src.src_country_code) AS src_country_code,
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any(toString(cityHash64(concat(toString(src.tcp_meta_window_size), toString(src.tcp_meta_mss), toString(src.tcp_meta_window_scale), src.tcp_meta_options)))) AS tcp_fp_raw,
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varPopState(toFloat64(src.syn_to_clienthello_ms)) AS tcp_jitter_variance,
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any(src.tcp_meta_window_size) AS tcp_win_raw, any(src.tcp_meta_window_scale) AS tcp_scale_raw,
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any(src.tcp_meta_mss) AS tcp_mss_raw, any(src.ip_meta_ttl) AS tcp_ttl_raw,
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any(src.http_version) AS http_ver_raw, any(src.tls_alpn) AS tls_alpn_raw,
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any(src.tls_sni) AS tls_sni_raw, any(src.header_user_agent) AS first_ua,
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max(toUInt8(src.correlated)) AS correlated_raw, uniqState(toUInt16(src.src_port)) AS unique_src_ports,
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uniqState(src.conn_id) AS unique_conn_id, max(toUInt32(src.keepalives)) AS max_keepalives,
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sum(IF(src.orphan_side = 'A' OR toUInt8(src.correlated) = 0, 1, 0)) AS orphan_count,
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sum(IF(src.ip_meta_id == 0, 1, 0)) AS ip_id_zero_count,
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varPopState(toFloat64(src.ip_meta_total_length)) AS total_ip_length_var,
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|
||||||
sum(IF(src.tcp_meta_mss == 1460, 1, 0)) AS mss_1460_count
|
|
||||||
FROM mabase_prod.http_logs AS src
|
|
||||||
GROUP BY window_start, src_ip, ja4, host;
|
|
||||||
|
|
||||||
-- 4. AGRÉGATION HEADERS (JA4H)
|
|
||||||
CREATE TABLE mabase_prod.agg_header_fingerprint_1h
|
|
||||||
(
|
|
||||||
window_start DateTime, src_ip String, header_order_hash SimpleAggregateFunction(any, String),
|
|
||||||
header_count SimpleAggregateFunction(max, UInt16), has_accept_language SimpleAggregateFunction(max, UInt8),
|
|
||||||
has_cookie SimpleAggregateFunction(max, UInt8), has_referer SimpleAggregateFunction(max, UInt8),
|
|
||||||
modern_browser_score SimpleAggregateFunction(max, UInt8), ua_ch_mismatch SimpleAggregateFunction(max, UInt8),
|
|
||||||
sec_fetch_mode SimpleAggregateFunction(any, String), sec_fetch_dest SimpleAggregateFunction(any, String)
|
|
||||||
) ENGINE = AggregatingMergeTree() ORDER BY (window_start, src_ip);
|
|
||||||
|
|
||||||
CREATE MATERIALIZED VIEW mabase_prod.mv_agg_header_fingerprint_1h TO mabase_prod.agg_header_fingerprint_1h AS
|
|
||||||
SELECT
|
|
||||||
toStartOfHour(src.time) AS window_start, src.src_ip, any(toString(cityHash64(src.client_headers))) AS header_order_hash,
|
|
||||||
max(toUInt16(length(src.client_headers) - length(replaceAll(src.client_headers, ',', '')) + 1)) AS header_count,
|
|
||||||
max(toUInt8(if(position(src.client_headers, 'Accept-Language') > 0, 1, 0))) AS has_accept_language,
|
|
||||||
max(toUInt8(if(position(src.client_headers, 'Cookie') > 0, 1, 0))) AS has_cookie,
|
|
||||||
max(toUInt8(if(position(src.client_headers, 'Referer') > 0, 1, 0))) AS has_referer,
|
|
||||||
max(toUInt8(if(length(src.header_sec_ch_ua) > 0, 100, if(length(src.header_user_agent) > 0, 50, 0)))) AS modern_browser_score,
|
|
||||||
max(toUInt8(if((position(src.header_user_agent, 'Windows') > 0 AND position(src.header_sec_ch_ua_platform, 'Windows') == 0) OR (position(src.header_user_agent, 'iPhone') > 0 AND position(src.header_sec_ch_ua_platform, 'iOS') == 0), 1, 0))) AS ua_ch_mismatch,
|
|
||||||
any(src.header_sec_fetch_mode) AS sec_fetch_mode, any(src.header_sec_fetch_dest) AS sec_fetch_dest
|
|
||||||
FROM mabase_prod.http_logs AS src
|
|
||||||
GROUP BY window_start, src.src_ip;
|
|
||||||
|
|
||||||
-- 5. TABLE RÉSULTATS DÉDOUBLONNÉE
|
|
||||||
CREATE TABLE mabase_prod.ml_detected_anomalies
|
|
||||||
(
|
|
||||||
detected_at DateTime, src_ip String, ja4 String, host String, anomaly_score Float32,
|
|
||||||
hits UInt64, hit_velocity Float32, fuzzing_index Float32, post_ratio Float32,
|
|
||||||
port_exhaustion_ratio Float32, max_keepalives UInt32, orphan_ratio Float32,
|
|
||||||
tcp_jitter_variance Float32, tcp_shared_count UInt32, true_window_size UInt64,
|
|
||||||
window_mss_ratio Float32, alpn_http_mismatch UInt8, is_alpn_missing UInt8,
|
|
||||||
sni_host_mismatch UInt8, header_count UInt16, has_accept_language UInt8,
|
|
||||||
has_cookie UInt8, has_referer UInt8, modern_browser_score UInt8,
|
|
||||||
is_headless UInt8, ua_ch_mismatch UInt8, header_order_shared_count UInt32,
|
|
||||||
ip_id_zero_ratio Float32, request_size_variance Float32, multiplexing_efficiency Float32,
|
|
||||||
mss_mobile_mismatch UInt8, reason String
|
|
||||||
) ENGINE = ReplacingMergeTree(detected_at) ORDER BY (src_ip, ja4, host) TTL detected_at + INTERVAL 30 DAY;
|
|
||||||
|
|
||||||
-- 6. VUE IA (24H + EXCLUSIONS + TOUT MERGE DANS SOUS-REQUÊTE)
|
|
||||||
CREATE OR REPLACE VIEW mabase_prod.view_ai_features_1h AS
|
|
||||||
SELECT
|
|
||||||
a.*, h.*,
|
|
||||||
(a.count_post / (a.hits + 1)) AS post_ratio, (a.uniq_query_params / (a.uniq_paths + 1)) AS fuzzing_index,
|
|
||||||
(a.hits / (dateDiff('second', a.first_seen, a.last_seen) + 1)) AS hit_velocity,
|
|
||||||
(a.unique_src_ports / (a.hits + 1)) AS port_exhaustion_ratio, (a.orphan_count / (a.hits + 1)) AS orphan_ratio,
|
|
||||||
(a.ip_id_zero_count / (a.hits + 1)) AS ip_id_zero_ratio, (a.hits / (a.unique_conn_id + 1)) AS multiplexing_efficiency,
|
|
||||||
IF(a.mss_1460_count > (a.hits * 0.8) AND h.modern_browser_score > 70, 1, 0) AS mss_mobile_mismatch,
|
|
||||||
count() OVER (PARTITION BY a.tcp_fingerprint) AS tcp_shared_count,
|
|
||||||
count() OVER (PARTITION BY h.header_order_hash) AS header_order_shared_count
|
|
||||||
FROM (
|
|
||||||
SELECT window_start, src_ip, ja4, host, sum(hits) AS hits, uniqMerge(uniq_paths) AS uniq_paths,
|
|
||||||
uniqMerge(uniq_query_params) AS uniq_query_params, sum(count_post) AS count_post,
|
|
||||||
min(first_seen) AS first_seen, max(last_seen) AS last_seen, any(tcp_fp_raw) AS tcp_fingerprint,
|
|
||||||
varPopMerge(tcp_jitter_variance) AS tcp_jitter_variance, varPopMerge(total_ip_length_var) AS request_size_variance,
|
|
||||||
any(tcp_win_raw * exp2(tcp_scale_raw)) AS true_window_size,
|
|
||||||
IF(any(tcp_mss_raw) > 0, any(tcp_win_raw) / any(tcp_mss_raw), 0) AS window_mss_ratio,
|
|
||||||
any(http_ver_raw) AS http_version, any(tls_alpn_raw) AS tls_alpn, any(tls_sni_raw) AS tls_sni,
|
|
||||||
max(correlated_raw) AS correlated, uniqMerge(unique_src_ports) AS unique_src_ports,
|
|
||||||
uniqMerge(unique_conn_id) AS unique_conn_id, max(max_keepalives) AS max_keepalives,
|
|
||||||
sum(orphan_count) AS orphan_count, sum(ip_id_zero_count) AS ip_id_zero_count, sum(mss_1460_count) AS mss_1460_count
|
|
||||||
FROM mabase_prod.agg_host_ip_ja4_1h
|
|
||||||
WHERE window_start >= now() - INTERVAL 24 HOUR
|
|
||||||
AND src_ip NOT IN (SELECT ip FROM mabase_prod.bot_ip)
|
|
||||||
AND ja4 NOT IN (SELECT ja4 FROM mabase_prod.bot_ja4)
|
|
||||||
GROUP BY window_start, src_ip, ja4, host
|
|
||||||
) a
|
|
||||||
LEFT JOIN (
|
|
||||||
SELECT window_start, src_ip, any(header_order_hash) AS header_order_hash, max(header_count) AS header_count,
|
|
||||||
max(has_accept_language) AS has_accept_language, max(has_cookie) AS has_cookie,
|
|
||||||
max(has_referer) AS has_referer, max(modern_browser_score) AS modern_browser_score,
|
|
||||||
max(ua_ch_mismatch) AS ua_ch_mismatch, any(sec_fetch_mode) AS sec_fetch_mode, any(sec_fetch_dest) AS sec_fetch_dest
|
|
||||||
FROM mabase_prod.agg_header_fingerprint_1h
|
|
||||||
WHERE window_start >= now() - INTERVAL 24 HOUR
|
|
||||||
GROUP BY window_start, src_ip
|
|
||||||
) h ON a.src_ip = h.src_ip AND a.window_start = h.window_start;
|
|
||||||
|
|
||||||
-- 7. RESTAURATION VUES HEURISTIQUES
|
|
||||||
CREATE OR REPLACE VIEW mabase_prod.view_host_ip_ja4_rotation AS
|
|
||||||
SELECT src_ip, uniqExact(ja4) AS distinct_ja4_count, sum(hits) AS total_hits FROM mabase_prod.agg_host_ip_ja4_1h
|
|
||||||
WHERE window_start >= now() - INTERVAL 24 HOUR GROUP BY src_ip HAVING distinct_ja4_count >= 5 AND total_hits > 100;
|
|
||||||
|
|
||||||
CREATE OR REPLACE VIEW mabase_prod.view_host_ja4_anomalies AS
|
|
||||||
SELECT ja4, uniqExact(src_ip) AS unique_ips, uniqExact(src_country_code) AS unique_countries, uniqExact(host) AS targeted_hosts
|
|
||||||
FROM mabase_prod.agg_host_ip_ja4_1h WHERE window_start >= now() - INTERVAL 24 HOUR GROUP BY ja4 HAVING unique_ips >= 20 AND targeted_hosts >= 3;
|
|
||||||
|
|
||||||
CREATE OR REPLACE VIEW mabase_prod.view_form_bruteforce_detected AS
|
|
||||||
SELECT src_ip, ja4, host, sum(hits) AS hits, uniqMerge(uniq_query_params) AS query_params_count FROM mabase_prod.agg_host_ip_ja4_1h
|
|
||||||
WHERE window_start >= now() - INTERVAL 24 HOUR GROUP BY src_ip, ja4, host HAVING query_params_count >= 10 AND hits >= 20;
|
|
||||||
|
|
||||||
CREATE OR REPLACE VIEW mabase_prod.view_alpn_mismatch_detected AS
|
|
||||||
SELECT src_ip, ja4, host, sum(hits) AS hits, any(http_ver_raw) AS http_version, any(tls_alpn_raw) AS tls_alpn FROM mabase_prod.agg_host_ip_ja4_1h
|
|
||||||
WHERE window_start >= now() - INTERVAL 24 HOUR GROUP BY src_ip, ja4, host HAVING http_version = '1.1' AND tls_alpn IN ('h2', 'h3') AND hits >= 10;
|
|
||||||
|
|
||||||
CREATE OR REPLACE VIEW mabase_prod.view_tcp_spoofing_detected AS
|
|
||||||
SELECT src_ip, ja4, any(tcp_ttl_raw) AS tcp_ttl, any(tcp_win_raw) AS tcp_window_size, any(first_ua) AS first_ua FROM mabase_prod.agg_host_ip_ja4_1h
|
|
||||||
WHERE window_start >= now() - INTERVAL 24 HOUR GROUP BY src_ip, ja4 HAVING tcp_ttl <= 64 AND (first_ua ILIKE '%Windows%' OR first_ua ILIKE '%iPhone%');
|
|
||||||
Reference in New Issue
Block a user