feat(e2e): add distributed E2E test framework with parametric traffic generation

Add run-e2e-test.sh with CLI parameters (--hits, --http-ratio, --dns, --tls,
--src-ips, --keep-analysis, --up) for configurable traffic generation. Traffic
runs from VM endpoints with multiple source IPs (alias IPs on eth0) to produce
distinct sessions for the ML pipeline. Fix curl TLS flags (--tlsv1.2 instead
of --tls-v1-2), skip redundant local verification in distributed mode, and
fix dashboard is_available() cache that never retried after ClickHouse recovery.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

services/bot-detector/bot_detector/metrics.py

@@ -106,31 +106,40 @@ def record_cycle_metrics(
     _emit_alerts(model_name, anomaly_rate, drift_rate, correlated_rate, latency_ms, drift_alert)
 
     try:
-        client.execute(
-            f"INSERT INTO {db}.ml_performance_metrics VALUES",
-            [{
-                'cycle_at':           now,
-                'model_name':         model_name,
-                'total_sessions':     n_total,
-                'correlated_rate':    round(float(correlated_rate), 4),
-                'anomaly_rate':       round(float(anomaly_rate), 4),
-                'critical_count':     n_critical,
-                'high_count':         n_high,
-                'medium_count':       n_medium,
-                'low_count':          n_low,
-                'known_bot_count':    n_known_bot,
-                'anubis_deny_count':  n_anubis_deny,
-                'legit_browser_count': n_legit_browser,
-                'drift_rate':         round(float(drift_rate), 4),
-                'drift_alert':        drift_alert,
-                'cycle_latency_ms':   latency_ms,
-                'features_valid':     valid_features,
-                'features_total':     total_features,
-                'baseline_size':      baseline_size,
-                'threshold':          round(float(threshold), 6),
-                'meta_learner_active': 1 if meta_learner_active else 0,
-            }]
+        # Vérifier que la table existe avant d'insérer (optionnelle)
+        table_check = client.query(
+            f"SELECT name FROM system.tables WHERE database = '{db}' AND name = 'ml_performance_metrics'"
         )
+        if not table_check.result_rows:
+            logger.debug("[Metrics] Table ml_performance_metrics absente — métriques non enregistrées")
+            return
+
+        client.insert(
+            f"{db}.ml_performance_metrics",
+            [[
+                now,
+                model_name,
+                n_total,
+                round(float(correlated_rate), 4),
+                round(float(anomaly_rate), 4),
+                n_critical,
+                n_high,
+                n_medium,
+                n_low,
+                n_known_bot,
+                n_anubis_deny,
+                n_legit_browser,
+                round(float(drift_rate), 4),
+                drift_alert,
+                latency_ms,
+                valid_features,
+                total_features,
+                baseline_size,
+                round(float(threshold), 6),
+                1 if meta_learner_active else 0,
+            ]]
+        )
+        logger.debug(f"[Metrics] Cycle {cycle_id} enregistré ({latency_ms}ms)")
     except Exception as e:
         logger.warning(f"[Metrics] Erreur d'enregistrement des métriques : {e}")