039086a0b3
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>
118 lines
6.0 KiB
Python
118 lines
6.0 KiB
Python
"""Prétraitement des données et listes de features.
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Normalise les colonnes, enrichit via l'identification multifactorielle des
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navigateurs, et définit les listes de features pour chaque modèle.
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"""
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import pandas as pd
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import numpy as np
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from .config import BROWSER_CONFIDENCE_THRESHOLD
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from .log import log_info
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from .browser import _compute_browser_axes, _parse_ja4_columns, _infer_browser_family
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# ═══════════════════════════════════════════════════════════════════════════════
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# LISTES DE FEATURES PAR MODÈLE
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# ═══════════════════════════════════════════════════════════════════════════════
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# Features communes (L7 HTTP pur, disponibles correlated=0 et 1)
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FEATURES = [
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'hits', 'hit_velocity', 'fuzzing_index', 'post_ratio', 'port_exhaustion_ratio',
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'orphan_ratio', 'max_keepalives', 'tcp_shared_count', 'header_order_shared_count',
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'header_count', 'has_accept_language', 'has_cookie', 'has_referer',
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'modern_browser_score', 'ua_ch_mismatch', 'ip_id_zero_ratio',
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'request_size_variance', 'multiplexing_efficiency', 'mss_mobile_mismatch',
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'asset_ratio', 'direct_access_ratio', 'is_ua_rotating', 'distinct_ja4_count',
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'src_port_density', 'ja4_asn_concentration', 'ja4_country_concentration', 'is_rare_ja4',
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'header_order_confidence', 'distinct_header_orders', 'temporal_entropy',
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'path_diversity_ratio', 'url_depth_variance', 'anomalous_payload_ratio',
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# B4-B7 : features L7 pures
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'head_ratio', 'sec_fetch_absence_rate', 'generic_accept_ratio', 'http10_ratio',
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# Anubis
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'anubis_is_flagged',
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# Browser multifactoriel
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'is_known_browser', 'browser_consistency_score', 'browser_confidence',
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'axis_ja4_known', 'axis_ja4_struct', 'axis_http_modern',
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'axis_nav_behavior', 'axis_tls_coherence',
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# HTTP
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'missing_accept_enc_ratio', 'http_scheme_ratio',
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# Thèse §5
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'path_transition_entropy',
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'cadence_cv', 'burst_ratio', 'pause_ratio',
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'lag1_autocorrelation', 'benford_deviation',
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'host_diversity', 'host_sweep_speed', 'host_coverage_uniformity',
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# P0+P1 : features sous-exploitées (SQL existant ou ajouté)
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'is_fake_navigation',
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'true_window_size', 'window_mss_ratio',
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# P1 : nouvelles features de détection
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'has_xff', 'unusual_content_type_ratio', 'non_standard_port_ratio',
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'login_post_concentration', 'sec_ch_mobile_mismatch',
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]
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# Features supplémentaires pour le modèle Complet (données TCP/TLS requises)
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FEATURES_COMPLET = FEATURES + [
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'tcp_jitter_variance', 'alpn_http_mismatch', 'is_alpn_missing', 'sni_host_mismatch',
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# B1-B3, B8 : features TLS/TCP
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'ja3_diversity_ratio', 'syn_timing_cv', 'tls12_ratio', 'ip_df_variance',
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# TTL fingerprinting OS + TCP window scale
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'avg_ttl', 'ttl_std', 'no_window_scale_ratio',
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# §5.5 — Dérive JA4 intra-session
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'ja4_drift_ratio',
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]
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# ═══════════════════════════════════════════════════════════════════════════════
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# PRÉTRAITEMENT
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# ═══════════════════════════════════════════════════════════════════════════════
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def preprocess_df(df: pd.DataFrame) -> pd.DataFrame:
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"""Normalise les colonnes et remplit les valeurs manquantes (commun 1h et 24h)."""
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df.columns = [c.split('.')[-1] for c in df.columns]
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for col in ['src_ip', 'ja4', 'host', 'bot_name', 'anubis_bot_name', 'anubis_bot_action',
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'anubis_bot_category', 'asn_number', 'asn_org', 'asn_detail', 'asn_domain',
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'country_code', 'asn_label']:
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if col in df.columns:
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df[col] = df[col].fillna('').astype(str)
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# ── A9 — Identification multifactorielle des navigateurs ──────────────────
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browser_axes = _compute_browser_axes(df)
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ja4_parsed = _parse_ja4_columns(df.get('ja4', pd.Series('', index=df.index)))
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df['inferred_browser_family'] = _infer_browser_family(df, ja4_parsed, browser_axes)
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df['browser_confidence'] = browser_axes['browser_confidence']
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for ax in ['axis_ja4_known', 'axis_ja4_struct', 'axis_http_modern',
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'axis_nav_behavior', 'axis_tls_coherence']:
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df[ax] = browser_axes[ax]
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# Rétro-compatibilité
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df['is_known_browser'] = browser_axes['axis_ja4_known'].astype(int)
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df['browser_consistency_score'] = (
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browser_axes['axis_ja4_known'].clip(0, 1)
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+ browser_axes['axis_http_modern'].apply(lambda x: 1 if x >= 0.5 else 0)
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+ browser_axes['axis_nav_behavior'].apply(lambda x: 1 if x >= 0.5 else 0)
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+ browser_axes['axis_tls_coherence'].apply(lambda x: 1 if x >= 0.5 else 0)
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+ (df['inferred_browser_family'] != '').astype(int)
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).astype(int)
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# anubis_is_flagged : signal de suspicion modéré
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df['anubis_is_flagged'] = (
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(df.get('anubis_bot_name', pd.Series('', index=df.index)) != '') &
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(~df.get('anubis_bot_action', pd.Series('', index=df.index)).isin(['ALLOW', 'DENY', '']))
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).astype(int)
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# Imputation intelligente
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binary_features = {
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'has_accept_language', 'has_cookie', 'has_referer', 'ua_ch_mismatch',
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'is_ua_rotating', 'is_alpn_missing', 'sni_host_mismatch', 'alpn_http_mismatch',
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'mss_mobile_mismatch', 'anubis_is_flagged', 'is_rare_ja4',
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'is_fake_navigation', 'has_xff', 'sec_ch_mobile_mismatch',
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}
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for col in df.columns:
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if col in binary_features:
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df[col] = df[col].fillna(-1)
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elif df[col].dtype in ('float64', 'float32', 'int64', 'int32', 'uint64', 'uint32'):
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df[col] = df[col].replace([np.inf, -np.inf], np.nan).fillna(df[col].median())
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return df
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