Add script to train hybrid ML detector with real data

Create a bash script to automate the training of the hybrid ML detector, automatically fetching database credentials from the .env file and executing the training process.

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deployment/train_hybrid_production.sh

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+#!/bin/bash
+#
+# Training Hybrid ML Detector su Dati Reali
+# Legge credenziali da /opt/ids/.env automaticamente
+#
+
+set -e  # Exit on error
+
+echo "======================================================================="
+echo "  TRAINING HYBRID ML DETECTOR - DATI REALI"
+echo "======================================================================="
+echo ""
+
+# Percorsi
+IDS_ROOT="/opt/ids"
+ENV_FILE="$IDS_ROOT/.env"
+PYTHON_ML_DIR="$IDS_ROOT/python_ml"
+VENV_PYTHON="$PYTHON_ML_DIR/venv/bin/python"
+
+# Verifica file .env esiste
+if [ ! -f "$ENV_FILE" ]; then
+    echo "❌ ERRORE: File .env non trovato in $ENV_FILE"
+    exit 1
+fi
+
+# Carica variabili da .env
+echo "📂 Caricamento credenziali database da .env..."
+source "$ENV_FILE"
+
+# Estrai credenziali database
+DB_HOST="${PGHOST:-localhost}"
+DB_PORT="${PGPORT:-5432}"
+DB_NAME="${PGDATABASE:-ids}"
+DB_USER="${PGUSER:-postgres}"
+DB_PASSWORD="${PGPASSWORD}"
+
+# Verifica password estratta
+if [ -z "$DB_PASSWORD" ]; then
+    echo "❌ ERRORE: PGPASSWORD non trovata nel file .env"
+    echo "   Aggiungi: PGPASSWORD=tua_password_qui"
+    exit 1
+fi
+
+echo "✅ Credenziali caricate:"
+echo "   Host: $DB_HOST"
+echo "   Port: $DB_PORT"
+echo "   Database: $DB_NAME"
+echo "   User: $DB_USER"
+echo "   Password: ****** (nascosta)"
+echo ""
+
+# Parametri training
+DAYS="${1:-7}"  # Default 7 giorni, puoi passare come argomento
+MAX_SAMPLES="${2:-1000000}"  # Default 1M records max
+
+echo "🎯 Parametri training:"
+echo "   Periodo: ultimi $DAYS giorni"
+echo "   Max records: $MAX_SAMPLES"
+echo ""
+
+# Verifica venv Python
+if [ ! -f "$VENV_PYTHON" ]; then
+    echo "❌ ERRORE: Virtual environment non trovato in $VENV_PYTHON"
+    echo "   Esegui prima: cd $IDS_ROOT && python3 -m venv python_ml/venv"
+    exit 1
+fi
+
+echo "🐍 Python: $VENV_PYTHON"
+echo ""
+
+# Verifica dati disponibili nel database
+echo "📊 Verifica dati disponibili nel database..."
+PGPASSWORD="$DB_PASSWORD" psql -h "$DB_HOST" -p "$DB_PORT" -U "$DB_USER" -d "$DB_NAME" -c "
+SELECT 
+  TO_CHAR(MIN(timestamp), 'YYYY-MM-DD HH24:MI:SS') as primo_log,
+  TO_CHAR(MAX(timestamp), 'YYYY-MM-DD HH24:MI:SS') as ultimo_log,
+  EXTRACT(DAY FROM (MAX(timestamp) - MIN(timestamp))) || ' giorni' as periodo_totale,
+  TO_CHAR(COUNT(*), 'FM999,999,999') as totale_records
+FROM network_logs;
+" 2>/dev/null
+
+if [ $? -ne 0 ]; then
+    echo "⚠️  WARNING: Impossibile verificare dati database (continuo comunque...)"
+fi
+
+echo ""
+echo "🚀 Avvio training..."
+echo ""
+echo "======================================================================="
+
+# Cambia directory
+cd "$PYTHON_ML_DIR"
+
+# Esegui training
+"$VENV_PYTHON" train_hybrid.py --train --source database \
+  --db-host "$DB_HOST" \
+  --db-port "$DB_PORT" \
+  --db-name "$DB_NAME" \
+  --db-user "$DB_USER" \
+  --db-password "$DB_PASSWORD" \
+  --days "$DAYS"
+
+# Check exit code
+if [ $? -eq 0 ]; then
+    echo ""
+    echo "======================================================================="
+    echo "✅ TRAINING COMPLETATO CON SUCCESSO!"
+    echo "======================================================================="
+    echo ""
+    echo "📁 Modelli salvati in: $PYTHON_ML_DIR/models/"
+    echo ""
+    echo "🔄 PROSSIMI PASSI:"
+    echo "   1. Restart ML backend: sudo systemctl restart ids-ml-backend"
+    echo "   2. Verifica caricamento: sudo journalctl -u ids-ml-backend -f"
+    echo "   3. Test API: curl http://localhost:8000/health"
+    echo ""
+else
+    echo ""
+    echo "======================================================================="
+    echo "❌ ERRORE DURANTE IL TRAINING"
+    echo "======================================================================="
+    echo ""
+    echo "Controlla i log sopra per dettagli sull'errore."
+    exit 1
+fi