Enhanced Anomaly Detection in Industrial Control Systems aided by Machine Learning

Abstract

This paper explores the enhancement of anomaly detection in industrial control systems (ICSs) by integrating machine learning with traditional intrusion detection. Using a comprehensive dataset from the SWaT facility at iTrust labs, we leverage both network traffic and process data to improve the detection of malicious activities. This hybrid approach significantly improves detection capabilities by capturing both network anomalies and process deviations, addressing gaps in traditional intrusion detection systems for increasingly interconnected ICSs. The findings contribute to a deeper understanding of anomaly detection techniques, providing actionable insights to improve the security posture of critical infrastructure.