Machine-Learning Network Intrusion Detection Lab
Real-time network packet stream classifier using XGBoost and SHAP for explainable cyber-attack diagnosis.
Project Overview
Captures live PCAP network traffic using PyShark/Scapy, extracts flow features (packet size, inter-arrival time, port scans), classifies malicious traffic (DoSS, PortScan, Botnet), and provides SHAP feature attributions. ### Practical Student Engineering Solution Fully functional hardware demonstration prototype equipped with dedicated microcontrollers, precision sensors, actuators, and an interactive cloud/mobile telemetry dashboard. ### Key Learning Outcomes - Embedded C / MicroPython firmware architecture and sensor interfacing - IoT telemetry protocols (MQTT / HTTP / BLE / LoRaWAN) - Power regulation, hardware debugging, and PCB design principles - Comprehensive technical documentation aligned with IEEE academic standards
Modern commercial and industrial infrastructure requires automated, real-time sensing and telemetry. Conventional manual monitoring suffers from high latency, human error, and lack of predictive fault visibility for Machine-Learning Network Intrusion Detection Lab.
A turnkey engineering prototype integrating high-precision sensor modules, dedicated microcontroller unit, and cloud telemetry protocols to automate measurement, trigger alert thresholds, and provide remote dashboard control.