Enhanced Anomaly Detection in Industrial Control Systems aided by Machine Learning

Abstract

This thesis investigates the potential of enhancing anomaly detection in Industrial Control Systems (ICS) through the integration of Machine Learning (ML) with traditional Network Intrusion Detection Systems (NIDS) and Host Intrusion Detection Systems (HIDS). Collaborating with DNV, a leading classification company, and utilising a rich dataset from iTrust labs’ SWaT experiment, this project explores the use of both process data and traditional network traffic to improve the detection of malicious activities within industrial networks.