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Add a hybrid machine learning system to reduce false positives

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Add a new hybrid ML detector system using Extended Isolation Forest and feature selection to reduce false positives. Documented with a deployment checklist and updated API performance notes.

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# Deployment Checklist - Hybrid ML Detector
Sistema ML avanzato per riduzione falsi positivi 80-90% con Extended Isolation Forest
## 📋 Pre-requisiti
- [ ] Server AlmaLinux 9 con accesso SSH
- [ ] PostgreSQL con database IDS attivo
- [ ] Python 3.11+ installato
- [ ] Venv attivo: `/opt/ids/python_ml/venv`
- [ ] Almeno 7 giorni di traffico real nel database (per training su dati reali)
---
## 🔧 Step 1: Installazione Dipendenze
```bash
# SSH al server
ssh user@ids.alfacom.it
# Attiva venv
cd /opt/ids/python_ml
source venv/bin/activate
# Installa nuove librerie
pip install -r requirements.txt
# Verifica installazione
python -c "import xgboost; import eif; import joblib; print('✅ Dipendenze OK')"
```
**Dipendenze nuove**:
- `xgboost==2.0.3` - Gradient Boosting per ensemble classifier
- `eif==2.0.0` - Extended Isolation Forest
- `joblib==1.3.2` - Model persistence
---
## 🧪 Step 2: Quick Test (Dataset Sintetico)
Testa il sistema con dataset sintetico per verificare funzionamento:
```bash
cd /opt/ids/python_ml
# Test rapido con 10k samples sintetici
python train_hybrid.py --test
# Cosa aspettarsi:
# - Dataset creato: 10000 samples (90% normal, 10% attacks)
# - Training completato su ~7000 normal samples
# - Detection results con confidence scoring
# - Validation metrics (Precision, Recall, F1, FPR)
```
**Output atteso**:
```
[TEST] Created synthetic dataset: 10,000 samples
Normal: 9,000 (90.0%)
Attacks: 1,000 (10.0%)
[TEST] Training on 6,300 normal samples...
[HYBRID] Training unsupervised model on 6,300 logs...
[HYBRID] Extracted features for X unique IPs
[HYBRID] Feature selection: 25 → 18 features
[HYBRID] Training Extended Isolation Forest...
[HYBRID] Training completed! X/Y IPs flagged as anomalies
[TEST] Detection results:
Total detections: XX
High confidence: XX
Medium confidence: XX
Low confidence: XX
╔══════════════════════════════════════════════════════════════╗
║ Synthetic Test Results ║
╚══════════════════════════════════════════════════════════════╝
🎯 Primary Metrics:
Precision: XX.XX% (of 100 flagged, how many are real attacks)
Recall: XX.XX% (of 100 attacks, how many detected)
F1-Score: XX.XX% (harmonic mean of P&R)
⚠️ False Positive Analysis:
FP Rate: XX.XX% (normal traffic flagged as attack)
```
**Criterio successo**:
- Precision ≥ 70% (test sintetico)
- FPR ≤ 10%
- Nessun crash
---
## 🎯 Step 3: Training su Traffico Reale
Addestra il modello sui log reali (ultimi 7 giorni):
```bash
cd /opt/ids/python_ml
# Training su database (ultimi 7 giorni)
python train_hybrid.py --train --source database \
--db-host localhost \
--db-port 5432 \
--db-name ids \
--db-user postgres \
--db-password "YOUR_PASSWORD" \
--days 7
# Modelli salvati in: python_ml/models/
# - isolation_forest_latest.pkl
# - scaler_latest.pkl
# - feature_selector_latest.pkl
# - metadata_latest.json
```
**Cosa succede**:
1. Carica ultimi 7 giorni di `network_logs` (fino a 1M records)
2. Estrae 25 features per ogni source_ip
3. Applica Chi-Square feature selection → 18 features
4. Addestra Extended Isolation Forest (contamination=3%)
5. Salva modelli in `models/`
**Criterio successo**:
- Training completato senza errori
- File modelli creati in `python_ml/models/`
- Log mostra "✅ Training completed!"
---
## 📊 Step 4: (Opzionale) Validazione CICIDS2017
Per validare con dataset scientifico (solo se si vuole benchmark accurato):
### 4.1 Download CICIDS2017
```bash
# Crea directory dataset
mkdir -p /opt/ids/python_ml/datasets/cicids2017
# Scarica manualmente da:
# https://www.unb.ca/cic/datasets/ids-2017.html
# Estrai i file CSV in: /opt/ids/python_ml/datasets/cicids2017/
# File richiesti (8 giorni):
# - Monday-WorkingHours.pcap_ISCX.csv
# - Tuesday-WorkingHours.pcap_ISCX.csv
# - ... (tutti i file CSV)
```
### 4.2 Validazione (10% sample per test)
```bash
cd /opt/ids/python_ml
# Validazione con 10% del dataset (test veloce)
python train_hybrid.py --validate --sample 0.1
# Validazione completa (LENTO - può richiedere ore!)
# python train_hybrid.py --validate
```
**Output atteso**:
```
╔══════════════════════════════════════════════════════════════╗
║ CICIDS2017 Validation Results ║
╚══════════════════════════════════════════════════════════════╝
🎯 Primary Metrics:
Precision: ≥90.00% ✅ TARGET
Recall: ≥80.00% ✅ TARGET
F1-Score: ≥85.00% ✅ TARGET
⚠️ False Positive Analysis:
FP Rate: ≤5.00% ✅ TARGET
[VALIDATE] Checking production deployment criteria...
✅ Model ready for production deployment!
```
**Criterio successo production**:
- Precision ≥ 90%
- Recall ≥ 80%
- FPR ≤ 5%
- F1-Score ≥ 85%
---
## 🚀 Step 5: Deploy in Produzione
### 5.1 Configura Environment Variable
```bash
# Aggiungi al .env del ML backend
echo "USE_HYBRID_DETECTOR=true" >> /opt/ids/python_ml/.env
# Oppure export manuale
export USE_HYBRID_DETECTOR=true
```
**Default**: `USE_HYBRID_DETECTOR=true` (nuovo detector attivo)
Per rollback: `USE_HYBRID_DETECTOR=false` (usa legacy detector)
### 5.2 Restart ML Backend
```bash
# Systemd service
sudo systemctl restart ids-ml-backend
# Verifica startup
sudo systemctl status ids-ml-backend
sudo journalctl -u ids-ml-backend -f
# Cerca log:
# "[ML] Using Hybrid ML Detector (Extended Isolation Forest + Feature Selection)"
# "[HYBRID] Models loaded (version: latest)"
```
### 5.3 Test API
```bash
# Test health check
curl http://localhost:8000/health
# Output atteso:
{
"status": "healthy",
"database": "connected",
"ml_model": "loaded",
"ml_model_type": "hybrid (EIF + Feature Selection)",
"timestamp": "2025-11-24T18:30:00"
}
# Test root endpoint
curl http://localhost:8000/
# Output atteso:
{
"service": "IDS API",
"version": "2.0.0",
"status": "running",
"model_type": "hybrid",
"model_loaded": true,
"use_hybrid": true
}
```
---
## 📈 Step 6: Monitoring & Validation
### 6.1 Primo Detection Run
```bash
# API call per detection (con API key se configurata)
curl -X POST http://localhost:8000/detect \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"max_records": 5000,
"hours_back": 1,
"risk_threshold": 60.0,
"auto_block": false
}'
```
### 6.2 Verifica Detections
```bash
# Query PostgreSQL per vedere detections
psql -d ids -c "
SELECT
source_ip,
risk_score,
confidence,
anomaly_type,
detected_at
FROM detections
ORDER BY detected_at DESC
LIMIT 10;
"
```
### 6.3 Monitoring Logs
```bash
# Monitora log ML backend
sudo journalctl -u ids-ml-backend -f | grep -E "(HYBRID|DETECT|TRAIN)"
# Log chiave:
# - "[HYBRID] Models loaded" - Modello caricato OK
# - "[DETECT] Using Hybrid ML Detector" - Detection con nuovo modello
# - "[DETECT] Detected X unique IPs above threshold" - Risultati
```
---
## 🔄 Step 7: Re-training Periodico
Il modello va ri-addestrato periodicamente (es. settimanalmente) su traffico recente:
### Opzione A: Manuale
```bash
# Ogni settimana
cd /opt/ids/python_ml
source venv/bin/activate
python train_hybrid.py --train --source database \
--db-password "YOUR_PASSWORD" \
--days 7
```
### Opzione B: Cron Job
```bash
# Crea script wrapper
cat > /opt/ids/scripts/retrain_ml.sh << 'EOF'
#!/bin/bash
set -e
cd /opt/ids/python_ml
source venv/bin/activate
python train_hybrid.py --train --source database \
--db-host localhost \
--db-port 5432 \
--db-name ids \
--db-user postgres \
--db-password "$PGPASSWORD" \
--days 7
# Restart backend per caricare nuovo modello
sudo systemctl restart ids-ml-backend
echo "[$(date)] ML model retrained successfully"
EOF
chmod +x /opt/ids/scripts/retrain_ml.sh
# Aggiungi cron (ogni domenica alle 3:00 AM)
sudo crontab -e
# Aggiungi riga:
0 3 * * 0 /opt/ids/scripts/retrain_ml.sh >> /var/log/ids/ml_retrain.log 2>&1
```
---
## 📊 Step 8: Confronto Vecchio vs Nuovo
Monitora metriche prima/dopo per 1-2 settimane:
### Metriche da tracciare:
1. **False Positive Rate** (obiettivo: -80%)
```sql
-- Query FP rate settimanale
SELECT
DATE(detected_at) as date,
COUNT(*) FILTER (WHERE is_false_positive = true) as false_positives,
COUNT(*) as total_detections,
ROUND(100.0 * COUNT(*) FILTER (WHERE is_false_positive = true) / COUNT(*), 2) as fp_rate
FROM detections
WHERE detected_at >= NOW() - INTERVAL '7 days'
GROUP BY DATE(detected_at)
ORDER BY date;
```
2. **Detection Count per Confidence Level**
```sql
SELECT
confidence,
COUNT(*) as count
FROM detections
WHERE detected_at >= NOW() - INTERVAL '24 hours'
GROUP BY confidence
ORDER BY
CASE confidence
WHEN 'high' THEN 1
WHEN 'medium' THEN 2
WHEN 'low' THEN 3
END;
```
3. **Blocked IPs Analysis**
```bash
# Query MikroTik per vedere IP bloccati
# Confronta con detections high-confidence
```
---
## 🔧 Troubleshooting
### Problema: "ModuleNotFoundError: No module named 'eif'"
**Soluzione**:
```bash
cd /opt/ids/python_ml
source venv/bin/activate
pip install eif==2.0.0
```
### Problema: "Modello non addestrato. Esegui /train prima."
**Soluzione**:
```bash
# Verifica modelli esistano
ls -lh /opt/ids/python_ml/models/
# Se vuoti, esegui training
python train_hybrid.py --train --source database --db-password "PWD"
```
### Problema: API restituisce errore 500
**Soluzione**:
```bash
# Check logs
sudo journalctl -u ids-ml-backend -n 100
# Verifica USE_HYBRID_DETECTOR
grep USE_HYBRID_DETECTOR /opt/ids/python_ml/.env
# Fallback a legacy
echo "USE_HYBRID_DETECTOR=false" >> /opt/ids/python_ml/.env
sudo systemctl restart ids-ml-backend
```
### Problema: Metrics validation non passa (Precision < 90%)
**Soluzione**: Tuning hyperparameters
```python
# In ml_hybrid_detector.py, modifica config:
'eif_contamination': 0.02, # Prova valori 0.01-0.05
'chi2_top_k': 20, # Prova 15-25
'confidence_high': 97.0, # Aumenta soglia confidence
```
---
## ✅ Checklist Finale
- [ ] Test sintetico passato (Precision ≥70%)
- [ ] Training su dati reali completato
- [ ] Modelli salvati in `python_ml/models/`
- [ ] `USE_HYBRID_DETECTOR=true` configurato
- [ ] ML backend restartato con successo
- [ ] API `/health` mostra `"ml_model_type": "hybrid"`
- [ ] Primo detection run completato
- [ ] Detections salvate in database con confidence levels
- [ ] (Opzionale) Validazione CICIDS2017 con metrics target raggiunti
- [ ] Re-training periodico configurato (cron o manuale)
- [ ] Dashboard frontend mostra detections con nuovi confidence levels
---
## 📚 Documentazione Tecnica
### Architettura
```
┌─────────────────┐
│ Network Logs │
│ (PostgreSQL) │
└────────┬────────┘
│
v
┌─────────────────┐
│ Feature Extract │ 25 features per IP
│ (25 features) │ (volume, temporal, protocol, behavioral)
└────────┬────────┘
│
v
┌─────────────────┐
│ Chi-Square Test │ Feature Selection
│ (Select Top 18)│ Riduce dimensionalità
└────────┬────────┘
│
v
┌─────────────────┐
│ Extended IF │ Unsupervised Anomaly Detection
│ (contamination │ n_estimators=250
│ = 0.03) │ anomaly_score: 0-100
└────────┬────────┘
│
v
┌─────────────────┐
│ Confidence Score│ 3-tier system
│ High ≥95% │ - High: auto-block
│ Medium ≥70% │ - Medium: manual review
│ Low <70% │ - Low: monitor
└────────┬────────┘
│
v
┌─────────────────┐
│ Detections │ Salvate in DB
│ (Database) │ Con geo info + confidence
└─────────────────┘
```
### Hyperparameters Tuning
| Parametro | Valore Default | Range Consigliato | Effetto |
|-----------|----------------|-------------------|---------|
| `eif_contamination` | 0.03 | 0.01 - 0.05 | % di anomalie attese. ↑ = più rilevamenti |
| `eif_n_estimators` | 250 | 100 - 500 | Numero alberi. ↑ = più stabile ma lento |
| `chi2_top_k` | 18 | 15 - 25 | Numero features selezionate |
| `confidence_high` | 95.0 | 90.0 - 98.0 | Soglia auto-block. ↑ = più conservativo |
| `confidence_medium` | 70.0 | 60.0 - 80.0 | Soglia review manuale |
---
## 🎯 Target Metrics Recap
| Metrica | Target Production | Test Sintetico | Note |
|---------|-------------------|----------------|------|
| **Precision** | ≥ 90% | ≥ 70% | Di 100 flagged, quanti sono veri attacchi |
| **Recall** | ≥ 80% | ≥ 60% | Di 100 attacchi, quanti rilevati |
| **F1-Score** | ≥ 85% | ≥ 65% | Media armonica Precision/Recall |
| **FPR** | ≤ 5% | ≤ 10% | Falsi positivi su traffico normale |
---
## 📞 Support
Per problemi o domande:
1. Check logs: `sudo journalctl -u ids-ml-backend -f`
2. Verifica modelli: `ls -lh /opt/ids/python_ml/models/`
3. Test manuale: `python train_hybrid.py --test`
4. Rollback: `USE_HYBRID_DETECTOR=false` + restart
**Ultimo aggiornamento**: 24 Nov 2025 - v2.0.0

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@ -85,4 +85,21 @@ The IDS employs a React-based frontend for real-time monitoring, detection visua
- **API**: ip-api.com con batch async lookup (100 IP in ~1.5s invece di 150s!) - **API**: ip-api.com con batch async lookup (100 IP in ~1.5s invece di 150s!)
- **Performance**: Caching intelligente + fallback robusto - **Performance**: Caching intelligente + fallback robusto
- **Display**: Globe/Building/MapPin icons nella pagina Detections - **Display**: Globe/Building/MapPin icons nella pagina Detections
- **Deploy**: Migration 004 + restart ML backend - **Deploy**: Migration 004 + restart ML backend
### 🤖 Hybrid ML Detector - False Positive Reduction System (24 Nov 2025 - 18:30)
- **Obiettivo**: Riduzione falsi positivi 80-90% mantenendo alta detection accuracy
- **Architettura Nuova**:
1. **Extended Isolation Forest**: n_estimators=250, contamination=0.03 (scientificamente tuned)
2. **Feature Selection**: Chi-Square test riduce 25→18 feature più rilevanti
3. **Confidence Scoring**: 3-tier system (High≥95%, Medium≥70%, Low<70%)
4. **Validation Framework**: CICIDS2017 dataset con Precision/Recall/F1/FPR metrics
- **Componenti**:
- `python_ml/ml_hybrid_detector.py` - Core detector con EIF + feature selection
- `python_ml/dataset_loader.py` - CICIDS2017 loader con mappatura 80→25 features
- `python_ml/validation_metrics.py` - Production-grade metrics calculator
- `python_ml/train_hybrid.py` - CLI training script (test/train/validate)
- **Dipendenze Nuove**: xgboost==2.0.3, joblib==1.3.2, eif==2.0.0
- **Backward Compatibility**: USE_HYBRID_DETECTOR env var (default=true)
- **Target Metrics**: Precision≥90%, Recall≥80%, FPR≤5%, F1≥85%
- **Deploy**: Vedere `deployment/CHECKLIST_ML_HYBRID.md`