Deep Reinforcement Learning-Based Adversarial Attack and Defense in Industrial Control Systems

Adversarial attacks targeting industrial control systems, such as the Maroochy wastewater system attack and the Stuxnet worm attack, have caused significant damage to related facilities. To enhance the security of industrial control systems, recent research has focused on not only improving the accuracy of intrusion detection systems but also developing techniques to generate adversarial attacks for evaluating the performance of these intrusion detection systems. In this paper, we propose a deep reinforcement learning-based adversarial attack framework designed to perform man-in-the-middle attacks on industrial control systems. Unlike existing adversarial attack methods, our proposed adversarial attack scheme learns to evade detection by the intrusion detection system based on both the impact on the target and the detection results from previous attacks. For performance evaluation, we utilized a dataset collected from the secure water treatment (SWaT) testbed. The simulation results demonstrated that our adversarial attack scheme successfully executed man-in-the-middle attacks while evading detection by the rule-based intrusion detection system, which was defined based on the analysis of the SWaT dataset.