Explainable Anomaly Detection for Industrial Control System Cybersecurity

作     者：Do Thu Ha Nguyen Xuan Hoang Nguyen Viet Hoang Nguyen Huu Du Truong Thu Huong Kim Phuc Tran 

作者机构：International Research Institute for Artificial Intelligence and Data Science Dong A University Danang Vietnam Hanoi University of Science and Technology Vietnam Univ. Lille ENSAIT ULR 2461 - GEMTEX - Génie et Matériaux Textiles F-59000 Lille France 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2022年第55卷第10期

页      面：1183-1188页

主　　题：XAI LSTM Autoencoder Anomaly Detection ICS Gradient SHAP 

摘      要：Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites. While massive digital data can be a driving force for system performance, data security has raised serious concerns. Anomaly detection, therefore, is essential for preventing network security intrusions and system attacks. Many AI-based anomaly detection methods have been proposed and achieved high detection performance, however, are still a black box that is hard to be interpreted. In this study, we suggest using Explainable Artificial Intelligence to enhance the perspective and reliable results of an LSTM-based Autoencoder-OCSVM learning model for anomaly detection in ICS. We demonstrate the performance of our proposed method based on a well-known SCADA dataset.