7e9599804a
- Tipo: patch - Database schema: database-schema/schema.sql (solo struttura) - Data: 2025-11-24 18:07:00
126 lines
3.6 KiB
Bash
Executable File
126 lines
3.6 KiB
Bash
Executable File
#!/bin/bash
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#
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# Training Hybrid ML Detector su Dati Reali
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# Legge credenziali da /opt/ids/.env automaticamente
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#
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set -e # Exit on error
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echo "======================================================================="
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echo " TRAINING HYBRID ML DETECTOR - DATI REALI"
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echo "======================================================================="
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echo ""
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# Percorsi
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IDS_ROOT="/opt/ids"
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ENV_FILE="$IDS_ROOT/.env"
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PYTHON_ML_DIR="$IDS_ROOT/python_ml"
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VENV_PYTHON="$PYTHON_ML_DIR/venv/bin/python"
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# Verifica file .env esiste
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if [ ! -f "$ENV_FILE" ]; then
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echo "❌ ERRORE: File .env non trovato in $ENV_FILE"
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exit 1
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fi
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# Carica variabili da .env
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echo "📂 Caricamento credenziali database da .env..."
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source "$ENV_FILE"
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# Estrai credenziali database
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DB_HOST="${PGHOST:-localhost}"
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DB_PORT="${PGPORT:-5432}"
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DB_NAME="${PGDATABASE:-ids}"
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DB_USER="${PGUSER:-postgres}"
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DB_PASSWORD="${PGPASSWORD}"
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# Verifica password estratta
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if [ -z "$DB_PASSWORD" ]; then
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echo "❌ ERRORE: PGPASSWORD non trovata nel file .env"
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echo " Aggiungi: PGPASSWORD=tua_password_qui"
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exit 1
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fi
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echo "✅ Credenziali caricate:"
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echo " Host: $DB_HOST"
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echo " Port: $DB_PORT"
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echo " Database: $DB_NAME"
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echo " User: $DB_USER"
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echo " Password: ****** (nascosta)"
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echo ""
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# Parametri training
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DAYS="${1:-7}" # Default 7 giorni, puoi passare come argomento
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MAX_SAMPLES="${2:-1000000}" # Default 1M records max
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echo "🎯 Parametri training:"
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echo " Periodo: ultimi $DAYS giorni"
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echo " Max records: $MAX_SAMPLES"
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echo ""
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# Verifica venv Python
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if [ ! -f "$VENV_PYTHON" ]; then
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echo "❌ ERRORE: Virtual environment non trovato in $VENV_PYTHON"
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echo " Esegui prima: cd $IDS_ROOT && python3 -m venv python_ml/venv"
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exit 1
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fi
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echo "🐍 Python: $VENV_PYTHON"
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echo ""
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# Verifica dati disponibili nel database
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echo "📊 Verifica dati disponibili nel database..."
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PGPASSWORD="$DB_PASSWORD" psql -h "$DB_HOST" -p "$DB_PORT" -U "$DB_USER" -d "$DB_NAME" -c "
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SELECT
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TO_CHAR(MIN(timestamp), 'YYYY-MM-DD HH24:MI:SS') as primo_log,
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TO_CHAR(MAX(timestamp), 'YYYY-MM-DD HH24:MI:SS') as ultimo_log,
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EXTRACT(DAY FROM (MAX(timestamp) - MIN(timestamp))) || ' giorni' as periodo_totale,
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TO_CHAR(COUNT(*), 'FM999,999,999') as totale_records
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FROM network_logs;
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" 2>/dev/null
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if [ $? -ne 0 ]; then
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echo "⚠️ WARNING: Impossibile verificare dati database (continuo comunque...)"
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fi
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echo ""
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echo "🚀 Avvio training..."
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echo ""
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echo "======================================================================="
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# Cambia directory
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cd "$PYTHON_ML_DIR"
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# Esegui training
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"$VENV_PYTHON" train_hybrid.py --train --source database \
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--db-host "$DB_HOST" \
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--db-port "$DB_PORT" \
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--db-name "$DB_NAME" \
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--db-user "$DB_USER" \
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--db-password "$DB_PASSWORD" \
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--days "$DAYS"
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# Check exit code
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if [ $? -eq 0 ]; then
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echo ""
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echo "======================================================================="
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echo "✅ TRAINING COMPLETATO CON SUCCESSO!"
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echo "======================================================================="
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echo ""
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echo "📁 Modelli salvati in: $PYTHON_ML_DIR/models/"
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echo ""
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echo "🔄 PROSSIMI PASSI:"
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echo " 1. Restart ML backend: sudo systemctl restart ids-ml-backend"
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echo " 2. Verifica caricamento: sudo journalctl -u ids-ml-backend -f"
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echo " 3. Test API: curl http://localhost:8000/health"
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echo ""
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else
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echo ""
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echo "======================================================================="
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echo "❌ ERRORE DURANTE IL TRAINING"
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echo "======================================================================="
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echo ""
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echo "Controlla i log sopra per dettagli sull'errore."
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exit 1
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fi
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