Privacy-Preserving Industrial Control System Anomaly Detection Platform

Abstract

With the development of IT technologies, an increasing number of industrial control systems (ICSs) can be accessed from the public Internet (with authentication). In such an open environment, cyberattacks become a serious threat to both ICS system integrity and data privacy. As a countermeasure, anomaly detection systems are often deployed to analyze the network traffic. However, due to privacy regulation, the network packages cannot be directly processed in plaintext in many countries. In this work, we present a privacy-preserving anomaly detection platform for ICS. The platform consists of three nodes running low-latency MPC protocols to evaluate the live network packages using decision trees on the fly with privacy assurance. Our benchmark result shows that the platform can process thousands of packages every ten seconds.