A lightweight anomaly-based NIDS for end devices using machine learning, developed in Python. It monitors network traffic in real-time to detect DoS, Port Scan, Brute Force, and Web Attack. The machine learning component is trained on CIC-IDS2017 dataset. Evaluated on Raspberry Pi 4B for resource efficiency.

Features:

• Real-time network traffic capture and parsing
• Feature extraction for network flows (packet count, byte count, timing, flags, etc.)
• Machine learning-based anomaly and attack detection (binary and multi-class)
• Extensible feature engineering pipeline
• CSV logging
• Tkinter-based GUI for live monitoring and control

An n8n workflow that analyzes crypto market sentiment and price movements to suggest buy or sell decisions with target prices, accessible via Telegram bot.