A Digital Twin-Based Approach for Detecting Cyber–Physical Attacks in ICS Using Knowledge Discovery

Abstract

The integration and automation of industrial processes has brought significant gains in efficiency and productivity but also elevated cybersecurity risks, especially in the process industry. This paper introduces a methodology utilizing process mining and digital twins to enhance anomaly detection in Industrial Control Systems (ICS). By converting raw device logs into event logs, we uncover patterns and anomalies indicative of cyberattacks even when such attacks are masked by normal operational data. We present a detailed case study replicating an industrial process to demonstrate the practical application of our approach. Experimental results confirm the effectiveness of our method in identifying cyber–physical attacks within a realistic industrial setting.