ja4-platform/services/bot-detector/deploy_views.sql

-- ============================================================================
-- ARCHITECTURE DE DÉTECTION INTÉGRALE (v13 - bot_detector v11 + ml_all_scores)
-- Base : mabase_prod | Fenêtre : 24h | Dédoublonnage par src_ip
-- Modifications v11 : ajout campaign_id, raw_anomaly_score dans ml_detected_anomalies
--                    correction view_dashboard_variability (header_user_agent → reason)
-- Modifications v12 : ajout table ml_all_scores (toutes les classifications, sans seuil)
-- ============================================================================

-- 1. NETTOYAGE COMPLET
DROP TABLE IF EXISTS mabase_prod.ml_all_scores;
DROP DICTIONARY IF EXISTS mabase_prod.dict_bot_ip;
DROP DICTIONARY IF EXISTS mabase_prod.dict_bot_ja4;
DROP DICTIONARY IF EXISTS mabase_prod.dict_asn_reputation;
DROP TABLE IF EXISTS mabase_prod.ml_detected_anomalies;
DROP VIEW IF EXISTS mabase_prod.view_ip_recurrence;
DROP VIEW IF EXISTS mabase_prod.view_ai_features_1h;
-- Suppression des anciennes vues heuristiques
DROP VIEW IF EXISTS mabase_prod.view_host_ip_ja4_rotation;
DROP VIEW IF EXISTS mabase_prod.view_host_ja4_anomalies;
DROP VIEW IF EXISTS mabase_prod.view_form_bruteforce_detected;
DROP VIEW IF EXISTS mabase_prod.view_alpn_mismatch_detected;
DROP VIEW IF EXISTS mabase_prod.view_tcp_spoofing_detected;

DROP VIEW IF EXISTS mabase_prod.mv_agg_host_ip_ja4_1h;
DROP TABLE IF EXISTS mabase_prod.agg_host_ip_ja4_1h;
DROP VIEW IF EXISTS mabase_prod.mv_agg_header_fingerprint_1h;
DROP TABLE IF EXISTS mabase_prod.agg_header_fingerprint_1h;

-- ============================================================================
-- 2. DICTIONNAIRES DE RÉPUTATION EN RAM
-- ============================================================================
CREATE DICTIONARY mabase_prod.dict_bot_ip (prefix String, bot_name String)
PRIMARY KEY prefix SOURCE(FILE(path '/var/lib/clickhouse/user_files/bot_ip.csv' format 'CSV'))
LAYOUT(IP_TRIE()) LIFETIME(MIN 300 MAX 300);

CREATE DICTIONARY mabase_prod.dict_bot_ja4 (ja4 String, bot_name String)
PRIMARY KEY ja4 SOURCE(FILE(path '/var/lib/clickhouse/user_files/bot_ja4.csv' format 'CSV'))
LAYOUT(COMPLEX_KEY_HASHED()) LIFETIME(MIN 300 MAX 300);

CREATE DICTIONARY mabase_prod.dict_asn_reputation (src_asn UInt64, label String)
PRIMARY KEY src_asn SOURCE(FILE(path '/var/lib/clickhouse/user_files/asn_reputation.csv' format 'CSV'))
LAYOUT(HASHED()) LIFETIME(MIN 300 MAX 300);

-- ============================================================================
-- 3. TABLE D'AGRÉGATION COMPORTEMENTALE (L4 / L5 / L7)
-- ============================================================================
CREATE TABLE mabase_prod.agg_host_ip_ja4_1h
(
    window_start DateTime,
    src_ip IPv6, ja4 String, host String, src_asn UInt32,
    src_country_code SimpleAggregateFunction(any, String),
    src_as_name SimpleAggregateFunction(any, String),
    src_org SimpleAggregateFunction(any, String),
    src_domain SimpleAggregateFunction(any, String),
    first_seen SimpleAggregateFunction(min, DateTime),
    last_seen SimpleAggregateFunction(max, DateTime),
    hits SimpleAggregateFunction(sum, UInt64),
    count_post SimpleAggregateFunction(sum, UInt64),
    uniq_paths AggregateFunction(uniq, String),
    uniq_query_params AggregateFunction(uniq, String),
    tcp_fp_raw SimpleAggregateFunction(any, String),
    tcp_jitter_variance AggregateFunction(varPop, Float64),
    tcp_win_raw SimpleAggregateFunction(any, UInt32),
    tcp_scale_raw SimpleAggregateFunction(any, UInt32),
    tcp_mss_raw SimpleAggregateFunction(any, UInt32),
    tcp_ttl_raw SimpleAggregateFunction(any, UInt32),
    http_ver_raw SimpleAggregateFunction(any, String),
    tls_alpn_raw SimpleAggregateFunction(any, String),
    tls_sni_raw SimpleAggregateFunction(any, String),
    first_ua SimpleAggregateFunction(any, String),
    correlated_raw SimpleAggregateFunction(max, UInt8),
    unique_src_ports AggregateFunction(uniq, UInt16),
    unique_conn_id AggregateFunction(uniq, String),
    max_keepalives SimpleAggregateFunction(max, UInt32),
    orphan_count SimpleAggregateFunction(sum, UInt64),
    ip_id_zero_count SimpleAggregateFunction(sum, UInt64),
    total_ip_length_var AggregateFunction(varPop, Float64),
    mss_1460_count SimpleAggregateFunction(sum, UInt64),
    count_assets SimpleAggregateFunction(sum, UInt64),
    count_no_referer SimpleAggregateFunction(sum, UInt64),
    uniq_ua AggregateFunction(uniq, String),
    max_requests_per_sec SimpleAggregateFunction(max, UInt32),
    url_depth_variance AggregateFunction(varPop, Float64),
    count_anomalous_payload SimpleAggregateFunction(sum, UInt64),
    -- B features (ajoutées v14)
    uniq_ja3 AggregateFunction(uniq, String),               -- B1: diversité JA3/JA4
    avg_syn_ms AggregateFunction(avg, Float64),             -- B2: SYN timing moyen (pour CV)
    tls12_count SimpleAggregateFunction(sum, UInt64),       -- B3: ratio TLS 1.2
    count_head SimpleAggregateFunction(sum, UInt64),        -- B4: ratio requêtes HEAD
    count_no_sec_fetch SimpleAggregateFunction(sum, UInt64),-- B5: absence Sec-Fetch-*
    count_generic_accept SimpleAggregateFunction(sum, UInt64),-- B6: Accept générique
    count_http10 SimpleAggregateFunction(sum, UInt64),      -- B7: ratio HTTP/1.0
    ip_df_var AggregateFunction(varPop, Float64)            -- B8: variance bit DF
)
ENGINE = AggregatingMergeTree()
ORDER BY (window_start, src_ip, ja4, host);

-- ============================================================================
-- 4. VUE MATÉRIALISÉE → agg_host_ip_ja4_1h
-- ============================================================================
CREATE MATERIALIZED VIEW mabase_prod.mv_agg_host_ip_ja4_1h
TO mabase_prod.agg_host_ip_ja4_1h AS
SELECT
    toStartOfHour(src.time) AS window_start,
    toIPv6(src.src_ip) AS src_ip, src.ja4, src.host, src.src_asn,
    any(src.src_country_code) AS src_country_code, any(src.src_as_name) AS src_as_name,
    any(src.src_org) AS src_org, any(src.src_domain) AS src_domain,
    min(src.time) AS first_seen, max(src.time) AS last_seen, count() AS hits,
    sum(IF(src.method = 'POST', 1, 0)) AS count_post,
    uniqState(src.path) AS uniq_paths, uniqState(src.query) AS uniq_query_params,
    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,
    varPopState(toFloat64(src.syn_to_clienthello_ms)) AS tcp_jitter_variance,
    any(src.tcp_meta_window_size) AS tcp_win_raw, any(src.tcp_meta_window_scale) AS tcp_scale_raw,
    any(src.tcp_meta_mss) AS tcp_mss_raw, any(src.ip_meta_ttl) AS tcp_ttl_raw,
    any(src.http_version) AS http_ver_raw, any(src.tls_alpn) AS tls_alpn_raw, any(src.tls_sni) AS tls_sni_raw,
    any(src.header_user_agent) AS first_ua, max(toUInt8(src.correlated)) AS correlated_raw,
    uniqState(toUInt16(src.src_port)) AS unique_src_ports, uniqState(src.conn_id) AS unique_conn_id,
    max(toUInt32(src.keepalives)) AS max_keepalives,
    sum(IF(src.orphan_side = 'A' OR src.correlated = 0, 1, 0)) AS orphan_count,
    sum(IF(src.ip_meta_id == 0, 1, 0)) AS ip_id_zero_count,
    varPopState(toFloat64(src.ip_meta_total_length)) AS total_ip_length_var,
    sum(IF(src.tcp_meta_mss == 1460, 1, 0)) AS mss_1460_count,
    sum(IF(match(src.path, '(?i)\.(png|jpg|jpeg|gif|css|js|ico|woff2|svg|eot)$'), 1, 0)) AS count_assets,
    sum(IF(position(src.client_headers, 'Referer') = 0, 1, 0)) AS count_no_referer,
    uniqState(src.header_user_agent) AS uniq_ua,
    0 AS max_requests_per_sec,
    varPopState(toFloat64(length(replaceAll(src.path, '/', '//')) - length(src.path))) AS url_depth_variance,
    sum(IF(src.ip_meta_total_length < 60 OR src.ip_meta_total_length > 1500, 1, 0)) AS count_anomalous_payload,
    -- B features
    uniqState(src.ja3) AS uniq_ja3,
    avgState(toFloat64(src.syn_to_clienthello_ms)) AS avg_syn_ms,
    sum(IF(src.tls_version = '1.2', 1, 0)) AS tls12_count,
    sum(IF(src.method = 'HEAD', 1, 0)) AS count_head,
    sum(IF(length(src.header_sec_fetch_site) = 0, 1, 0)) AS count_no_sec_fetch,
    sum(IF(length(src.header_accept) < 5, 1, 0)) AS count_generic_accept,
    sum(IF(src.http_version = 'HTTP/1.0', 1, 0)) AS count_http10,
    varPopState(toFloat64(src.ip_meta_df)) AS ip_df_var
FROM mabase_prod.http_logs AS src
GROUP BY window_start, src_ip, ja4, host, src_asn;

-- ============================================================================
-- 5. TABLE D'AGRÉGATION DES HEADERS (L7)
-- ============================================================================
CREATE TABLE mabase_prod.agg_header_fingerprint_1h
(
    window_start DateTime,
    src_ip IPv6,
    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,
    toIPv6(src.src_ip) AS 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;

-- ============================================================================
-- 6. TABLE DE RÉSULTATS ML — MENACES UNIQUEMENT (scores < seuil)
-- ============================================================================
CREATE TABLE mabase_prod.ml_detected_anomalies
(
    detected_at DateTime, src_ip IPv6, ja4 String, host String, bot_name String,
    anomaly_score Float32, threat_level String, model_name String, recurrence UInt32,
    asn_number String, asn_org String, asn_detail String, asn_domain String, country_code String, asn_label String, 
    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, correlated UInt8, reason String,
    asset_ratio Float32, direct_access_ratio Float32, is_ua_rotating UInt8, distinct_ja4_count UInt32,
    src_port_density Float32, ja4_asn_concentration Float32, ja4_country_concentration Float32, is_rare_ja4 UInt8,
    header_order_confidence Float32, distinct_header_orders UInt32, temporal_entropy Float32,
    path_diversity_ratio Float32, url_depth_variance Float32, anomalous_payload_ratio Float32,
    -- Colonnes ajoutées en v11 (bot_detector v11)
    campaign_id Int32 DEFAULT -1,
    raw_anomaly_score Float32 DEFAULT 0
)
ENGINE = ReplacingMergeTree(detected_at)
ORDER BY (src_ip)
TTL detected_at + INTERVAL 30 DAY;

-- ============================================================================
-- 6b. TABLE DE TOUTES LES CLASSIFICATIONS (sans seuil, pour observabilité)
-- ============================================================================
CREATE TABLE mabase_prod.ml_all_scores
(
    detected_at       DateTime,
    window_start      DateTime,
    src_ip            IPv6,
    ja4               String,
    host              String,
    bot_name          String,
    anomaly_score     Float32,
    raw_anomaly_score Float32,
    threat_level      String,
    model_name        String,
    correlated        UInt8,
    asn_number        String,
    asn_org           String,
    country_code      String,
    asn_label         String,
    hits              UInt64,
    hit_velocity      Float32,
    fuzzing_index     Float32,
    post_ratio        Float32,
    campaign_id       Int32
)
ENGINE = ReplacingMergeTree(detected_at)
ORDER BY (window_start, src_ip, ja4, host, model_name)
TTL window_start + INTERVAL 3 DAY
SETTINGS index_granularity = 8192;

-- ============================================================================
-- 7. VUE DE RÉCURRENCE
-- ============================================================================
CREATE OR REPLACE VIEW mabase_prod.view_ip_recurrence AS
SELECT src_ip, count() AS recurrence, min(detected_at) AS first_seen, max(detected_at) AS last_seen, 
min(anomaly_score) AS worst_score, argMin(threat_level, anomaly_score) AS worst_threat_level
FROM mabase_prod.ml_detected_anomalies GROUP BY src_ip;

-- ============================================================================
-- 8. VUE IA PRINCIPALE (Avec CTE pour Entropie Temporelle)
-- ============================================================================
CREATE OR REPLACE VIEW mabase_prod.view_ai_features_1h AS
WITH base_data AS (
    SELECT
        a.window_start, a.src_ip, a.ja4, a.host,
        toString(a.src_asn) AS asn_number, a.src_as_name AS asn_org,
        a.src_org AS asn_detail, a.src_domain AS asn_domain, a.src_country_code AS country_code,
        dictGetOrDefault('mabase_prod.dict_asn_reputation', 'label', toUInt64(a.src_asn), 'unknown') AS asn_label,
        COALESCE(
            nullIf(dictGetOrDefault('mabase_prod.dict_bot_ip', 'bot_name', a.src_ip, ''), ''),
            nullIf(dictGetOrDefault('mabase_prod.dict_bot_ja4', 'bot_name', tuple(a.ja4), ''), ''),
            ''
        ) AS bot_name,
        a.hits AS hits, 
        sum(a.hits) OVER (PARTITION BY a.src_ip) AS total_ip_hits,
        a.correlated AS correlated, a.tcp_jitter_variance AS tcp_jitter_variance, 
        a.true_window_size AS true_window_size, a.window_mss_ratio AS window_mss_ratio, a.max_keepalives AS max_keepalives,
        h.header_order_hash AS header_order_hash, h.header_count AS header_count,
        h.has_accept_language AS has_accept_language, h.has_cookie AS has_cookie,
        h.has_referer AS has_referer, h.modern_browser_score AS modern_browser_score, h.ua_ch_mismatch AS ua_ch_mismatch,
        (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,
        a.request_size_variance AS request_size_variance,
        IF(a.tls_alpn = 'h2' AND a.http_version != '2', 1, 0) AS alpn_http_mismatch,
        IF(length(a.tls_alpn) = 0 OR a.tls_alpn = '00', 1, 0) AS is_alpn_missing,
        IF(length(a.tls_sni) > 0 AND a.tls_sni != a.host, 1, 0) AS sni_host_mismatch,
        IF(h.sec_fetch_mode = 'navigate' AND h.sec_fetch_dest != 'document', 1, 0) AS is_fake_navigation,
        count() OVER (PARTITION BY a.tcp_fingerprint) AS tcp_shared_count,
        count() OVER (PARTITION BY h.header_order_hash) AS header_order_shared_count,
        (a.count_assets / (a.hits + 1)) AS asset_ratio, (a.count_no_referer / (a.hits + 1)) AS direct_access_ratio,
        IF(a.unique_ua > 2, 1, 0) AS is_ua_rotating, uniqExact(a.ja4) OVER (PARTITION BY a.src_ip) AS distinct_ja4_count,
        ((a.hits / (a.unique_src_ports + 1)) / (dateDiff('second', a.first_seen, a.last_seen) + 1)) AS src_port_density,
        (sum(a.hits) OVER (PARTITION BY a.ja4, a.src_asn) / (sum(a.hits) OVER (PARTITION BY a.ja4) + 1)) AS ja4_asn_concentration,
        (sum(a.hits) OVER (PARTITION BY a.ja4, a.src_country_code) / (sum(a.hits) OVER (PARTITION BY a.ja4) + 1)) AS ja4_country_concentration,
        IF(sum(a.hits) OVER (PARTITION BY a.ja4) < 100, 1, 0) AS is_rare_ja4,
        (count() OVER (PARTITION BY h.header_order_hash, a.first_ua) / (count() OVER (PARTITION BY a.first_ua) + 1)) AS header_order_confidence,
        uniqExact(h.header_order_hash) OVER (PARTITION BY a.src_ip) AS distinct_header_orders,
        (a.uniq_paths / (a.hits + 1)) AS path_diversity_ratio,
        a.url_depth_variance AS url_depth_variance,
        (a.count_anomalous_payload / (a.hits + 1)) AS anomalous_payload_ratio,
        -- B features : TLS/TCP (disponibles correlated=1 uniquement)
        a.uniq_ja3_val AS uniq_ja3_per_row,
        sqrt(a.tcp_jitter_variance) / greatest(a.avg_syn_ms_val, 1) AS syn_timing_cv,  -- B2
        a.tls12_count / (a.hits + 1) AS tls12_ratio,                                    -- B3
        -- B features : HTTP pures (disponibles correlated=0 et 1)
        a.count_head / (a.hits + 1) AS head_ratio,                                      -- B4
        a.count_no_sec_fetch / (a.hits + 1) AS sec_fetch_absence_rate,                  -- B5
        a.count_generic_accept / (a.hits + 1) AS generic_accept_ratio,                  -- B6
        a.count_http10 / (a.hits + 1) AS http10_ratio,                                  -- B7
        a.ip_df_variance AS ip_df_variance                                               -- B8
    FROM (
        SELECT
            window_start, src_ip, ja4, host, src_asn,
            any(src_country_code) AS src_country_code, any(src_as_name) AS src_as_name,
            any(src_org) AS src_org, any(src_domain) AS src_domain, any(first_ua) AS first_ua,
            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,
            sum(count_assets) AS count_assets, sum(count_no_referer) AS count_no_referer, uniqMerge(uniq_ua) AS unique_ua,
            varPopMerge(url_depth_variance) AS url_depth_variance,
            sum(count_anomalous_payload) AS count_anomalous_payload,
            -- B feature aggregates
            uniqMerge(uniq_ja3) AS uniq_ja3_val,
            avgMerge(avg_syn_ms) AS avg_syn_ms_val,
            sum(tls12_count) AS tls12_count,
            sum(count_head) AS count_head,
            sum(count_no_sec_fetch) AS count_no_sec_fetch,
            sum(count_generic_accept) AS count_generic_accept,
            sum(count_http10) AS count_http10,
            varPopMerge(ip_df_var) AS ip_df_variance
        FROM mabase_prod.agg_host_ip_ja4_1h
        WHERE window_start >= now() - INTERVAL 24 HOUR
        GROUP BY window_start, src_ip, ja4, host, src_asn
    ) 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
)
SELECT
    *,
    -(sum((hits / (total_ip_hits + 1)) * log2((hits / (total_ip_hits + 1)) + 0.000001)) OVER (PARTITION BY src_ip)) AS temporal_entropy,
    -- B1: ratio diversité JA3/JA4 par src_ip (signal: bots avec JA3 rotatifs sur peu de JA4)
    sum(uniq_ja3_per_row) OVER (PARTITION BY src_ip) / greatest(distinct_ja4_count, 1) AS ja3_diversity_ratio
FROM base_data;

-- ============================================================================
-- VUES POUR LE DASHBOARD WEB
-- ============================================================================

-- Vue pour les métriques globales du dashboard
CREATE OR REPLACE VIEW mabase_prod.view_dashboard_summary AS
SELECT
    count() AS total_detections,
    countIf(threat_level = 'CRITICAL') AS critical_count,
    countIf(threat_level = 'HIGH') AS high_count,
    countIf(threat_level = 'MEDIUM') AS medium_count,
    countIf(threat_level = 'LOW') AS low_count,
    countIf(bot_name != '') AS known_bots_count,
    countIf(bot_name = '') AS anomalies_count,
    uniq(src_ip) AS unique_ips
FROM mabase_prod.ml_detected_anomalies
WHERE detected_at >= now() - INTERVAL 24 HOUR;

-- Vue pour la série temporelle (par heure)
CREATE OR REPLACE VIEW mabase_prod.view_dashboard_timeseries AS
SELECT
    toStartOfHour(detected_at) AS hour,
    count() AS total,
    countIf(threat_level = 'CRITICAL') AS critical,
    countIf(threat_level = 'HIGH') AS high,
    countIf(threat_level = 'MEDIUM') AS medium,
    countIf(threat_level = 'LOW') AS low
FROM mabase_prod.ml_detected_anomalies
WHERE detected_at >= now() - INTERVAL 24 HOUR
GROUP BY hour
ORDER BY hour;

-- Vue pour la distribution des menaces
CREATE OR REPLACE VIEW mabase_prod.view_dashboard_threat_dist AS
SELECT
    threat_level,
    count() AS count,
    round(count() * 100.0 / sum(count()) OVER (), 2) AS percentage
FROM mabase_prod.ml_detected_anomalies
WHERE detected_at >= now() - INTERVAL 24 HOUR
GROUP BY threat_level
ORDER BY count DESC;

-- Vue pour la variabilité (utilisée par l'API)
-- Note v12 : header_user_agent n'existe pas dans ml_detected_anomalies → remplacé par reason
CREATE OR REPLACE VIEW mabase_prod.view_dashboard_variability AS
SELECT
    detected_at,
    src_ip,
    ja4,
    host,
    reason AS sample_reason,
    country_code,
    asn_number,
    asn_org,
    threat_level,
    model_name,
    anomaly_score,
    campaign_id,
    raw_anomaly_score
FROM mabase_prod.ml_detected_anomalies
WHERE detected_at >= now() - INTERVAL 24 HOUR;