IoT Intrusion Detection Using Edge/Fog Computing
Undergraduate research in distributed IoT traffic analysis and machine-learning intrusion detection.
This undergraduate research project explored intrusion detection across IoT endpoint, edge, fog, and cloud layers. The practical test environment used ESP32 nodes, a Raspberry Pi, and an Ubuntu computer, with network-flow analysis using CICFlowMeter and machine-learning-based classification.
The work combined embedded networking, Linux systems, traffic data collection, and applied machine learning for engineering-focused cybersecurity research.