Detecting Time-Delay Attacks in Industrial Control Systems Through State-Aware Inference

Abstract

The time-delay attacks pose serious security threats to the industrial control systems (ICSs), where ICS infrastructures (e.g., chemical factories) could suffer severe safety consequences. They could bypass current delay detection methods by avoiding triggering packet timeouts. In this article, we reveal that malicious states caused by the time-delay attacks in ICS scenarios can be detected by analyzing ICS programs. We propose detecting a time-delay attack in ICS scenarios by comparing the difference between malicious and benign states, meeting the real-time and noninterference requirements. Specifically, we utilize symbolic execution to analyze ICS programs to generate the benign states of ICS and leverage the key features of time-delay attacks to create the malicious states of ICS, where the states are transferred through the network for remote control and monitoring. We propose a multimodal neural network whose inputs are the malicious states sampled from the ICS network traffic and the time domain features, and the output is whether such a time-delay attack exists. We implement a prototype system and conduct real-world experiments to evaluate the performance of our detection approach. Our experiments cover 102 vulnerable ICS programs and five types of time-delay attacks. The evaluation results show that our approach can detect ICS time-delay attacks in 0.6 s, with 97.2% precision and 98% recall.