E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System

Abstract

Industrial Control Systems (ICS) are evolving into smart environments with increased interconnectivity by being connected to the Internet. These changes increase the likelihood of security vulnerabilities and accidents. As the risk of cyberattacks on ICS has increased, various anomaly detection studies are being conducted to detect abnormal situations in industrial processes. However, anomaly detection in ICS suffers from numerous false alarms. When false alarms occur, multiple sensors need to be checked, which is impractical. In this study, when an anomaly is detected, sensors displaying abnormal behavior are visually presented through XAI-based analysis to support quick practical actions and operations. Anomaly Detection has designed and applied better anomaly detection technology than the first prize at HAICon2020, an ICS security threat detection AI contest hosted by the National Security Research Institute last year, and explains the anomalies detected in its model. To the best of our knowledge, our work is at the forefront of explainable anomaly detection research in ICS. Therefore, it is expected to increase the utilization of anomaly detection technology in ICS.

Keywords

• Explainable anomaly detection
• ICS
• HAI dataset
• Bi-LSTM
• XAI
• SHAP