An Improved Industrial Control System Device Logs Processing Method for Process-Based Anomaly Detection

Detecting process-based attacks on industrial control systems (ICS) is challenging. These cyber-attacks are designed to disrupt the industrial process by changing the state of a system, while keeping the system's behaviour close to the expected behaviour. Such anomalous behaviour can be effectively detected by an event-driven approach. Petri Net (PN) model identification has proved to be an effective method for event-driven system analysis and anomaly detection. However, PN identification-based anomaly detection methods require ICS device logs to be converted into event logs (sequence of events). Therefore, in this paper we present a formalised method for pre-processing and transforming ICS device logs into event logs. The proposed approach outperforms the previous methods of device logs processing in terms of anomaly detection. We have demonstrated the results using two published datasets.