From 783d28f571b255dd977907b790efd8d41a935b41 Mon Sep 17 00:00:00 2001
From: marco370 <48531002-marco370@users.noreply.replit.com>
Date: Mon, 24 Nov 2025 16:06:29 +0000
Subject: [PATCH] Add a hybrid machine learning system to reduce false
 positives

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_ML_HYBRID.md | 536 ++++++++++++++++++++++++++++++
 replit.md                         |  19 +-
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 create mode 100644 deployment/CHECKLIST_ML_HYBRID.md

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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
diff --git a/replit.md b/replit.md
index 316379f..741343a 100644
--- a/replit.md
+++ b/replit.md
@@ -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!)
 - **Performance**: Caching intelligente + fallback robusto
 - **Display**: Globe/Building/MapPin icons nella pagina Detections
-- **Deploy**: Migration 004 + restart ML backend
\ No newline at end of file
+- **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`
\ No newline at end of file