Query-Based Black-Box Stealthy Sensor Attacks on Cyber-Physical Systems

We study the vulnerability of Cyber-physical systems (CPS) under stealthy sensor attacks in black-box scenarios. “Black-box” refers to scenarios where the attacker has minimal knowledge of the target system. Designing a stealthy sensor attack sequence under this scenario has two main challenges. The first one lies in ensuring the stealthiness of the sensor attack, meaning does not trigger an alert when applying the generated sensor attack sequence to the CPS. The second one is maintaining stealthiness throughout the attack generation process, indicating the limitation on the alarm frequency when generating the attack sequence. To address the above challenges, we develop a querybased black-box stealthy attack framework to violate the safety of the CPS. To maintain stealthiness during training, an active learning method has been introduced to extract the detector’s information to a time series model. The stealthy attack sequence is then generated from that model. Experiments on four numerical simulations and a high-fidelity simulator demonstrate the effectiveness of the proposed framework.