ids.alfacom.it/replit.md
marco370 9c5293158f Add a navigation sidebar and dashboard to the IDS system
Introduces a new sidebar component in `client/src/App.tsx` for navigation, along with new pages for Dashboard, Detections, and Routers. The backend in `server/routes.ts` is updated to include API endpoints for managing routers, fetching network logs, and retrieving detection data.

Replit-Commit-Author: Agent
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Replit-Commit-Screenshot-Url: https://storage.googleapis.com/screenshot-production-us-central1/449cf7c4-c97a-45ae-8234-e5c5b8d6a84f/7a657272-55ba-4a79-9a2e-f1ed9bc7a528/c9ITWqD
2025-11-15 11:16:44 +00:00

4.6 KiB

IDS - Intrusion Detection System

Sistema di rilevamento intrusioni per router MikroTik basato su Machine Learning.

Progetto

Tipo: Full-stack Web Application + Python ML Backend
Stack: React + FastAPI + PostgreSQL + MikroTik API REST

Architettura

Frontend (React)

  • Dashboard monitoring real-time
  • Visualizzazione detections e router
  • Gestione whitelist
  • ShadCN UI components
  • TanStack Query per data fetching

Backend Python (FastAPI)

  • ML Analyzer: Isolation Forest con 25 feature mirate
  • MikroTik Manager: Comunicazione API REST parallela con 10+ router
  • Detection Engine: Scoring 0-100 con 5 livelli di rischio
  • Endpoints: /train, /detect, /block-ip, /unblock-ip, /stats

Backend Node.js (Express)

  • API REST per frontend
  • Gestione database PostgreSQL
  • Routes: routers, detections, logs, whitelist, training-history

Database (PostgreSQL)

  • routers: Configurazione router MikroTik
  • network_logs: Log syslog da router
  • detections: Anomalie rilevate dal ML
  • whitelist: IP fidati
  • training_history: Storia training modelli

Workflow

  1. Log Collection: Router → Syslog → PostgreSQL network_logs
  2. Training: Python ML estrae 25 feature → Isolation Forest
  3. Detection: Analisi real-time → Scoring 0-100 → Classificazione
  4. Auto-Block: IP critico (>=80) → API REST → Tutti i router (parallelo)

File Importanti

Python ML Backend

  • python_ml/ml_analyzer.py: Core ML (25 feature, Isolation Forest)
  • python_ml/mikrotik_manager.py: Gestione router API REST
  • python_ml/main.py: FastAPI server
  • python_ml/requirements.txt: Dipendenze Python

Frontend

  • client/src/pages/Dashboard.tsx: Dashboard principale
  • client/src/pages/Detections.tsx: Lista rilevamenti
  • client/src/pages/Routers.tsx: Gestione router
  • client/src/App.tsx: App root con sidebar

Backend Node

  • server/routes.ts: API endpoints
  • server/storage.ts: Database operations
  • server/db.ts: PostgreSQL connection
  • shared/schema.ts: Drizzle ORM schema

Comandi Utili

Start Python Backend

cd python_ml
pip install -r requirements.txt
python main.py

API Calls

# Training
curl -X POST http://localhost:8000/train \
  -H "Content-Type: application/json" \
  -d '{"max_records": 10000, "hours_back": 24}'

# Detection
curl -X POST http://localhost:8000/detect \
  -H "Content-Type: application/json" \
  -d '{"max_records": 5000, "auto_block": true, "risk_threshold": 75}'

# Stats
curl http://localhost:8000/stats

Database

npm run db:push  # Sync schema to PostgreSQL

Configurazione Router MikroTik

Abilita API REST

/ip service
set api-ssl disabled=no
set www-ssl disabled=no

Aggiungi Router

Via dashboard web o SQL:

INSERT INTO routers (name, ip_address, username, password, api_port, enabled)
VALUES ('Router 1', '192.168.1.1', 'admin', 'password', 443, true);

Feature ML (25 totali)

Volume (5)

  • total_packets, total_bytes, conn_count
  • avg_packet_size, bytes_per_second

Temporali (8)

  • time_span_seconds, conn_per_second
  • hour_of_day, day_of_week
  • max_burst, avg_burst, burst_variance, avg_interval

Protocol Diversity (6)

  • unique_protocols, unique_dest_ports, unique_dest_ips
  • protocol_entropy, tcp_ratio, udp_ratio

Port Scanning (3)

  • unique_ports_contacted, port_scan_score, sequential_ports

Behavioral (3)

  • packets_per_conn, packet_size_variance, blocked_ratio

Livelli di Rischio

  • 🔴 CRITICO (85-100): Blocco immediato
  • 🟠 ALTO (70-84): Blocco + monitoring
  • 🟡 MEDIO (60-69): Monitoring
  • 🔵 BASSO (40-59): Logging
  • 🟢 NORMALE (0-39): Nessuna azione

Vantaggi vs Sistema Precedente

  • Feature: 150+ → 25 (mirate)
  • Training: ~5 min → ~10 sec
  • Detection: Lento → <2 sec
  • Router Comm: SSH → API REST
  • Multi-Router: Sequenziale → Parallelo
  • Database: MySQL → PostgreSQL
  • Falsi Negativi: Alti → Bassi

Note

  • Whitelist: IP protetti da blocco automatico
  • Timeout: Blocchi scadono dopo 1h (configurabile)
  • Parallel Blocking: Tutti i router aggiornati simultaneamente
  • Auto-Training: Configurabile via cron (consigliato ogni 12h)
  • Auto-Detection: Configurabile via cron (consigliato ogni 5 min)

Sicurezza

  • Password router gestite da database (non in codice)
  • API REST più sicura di SSH
  • Timeout automatico blocchi
  • Logging completo operazioni
  • PostgreSQL con connessione sicura

Development

  • Frontend: Workflow "Start application" (auto-reload)
  • Python Backend: python python_ml/main.py
  • API Docs: http://localhost:8000/docs
  • Database: PostgreSQL via Neon (environment variables auto-configurate)