Stealthy Hacking and Secrecy of Controlled State Estimation Systems With Random Dropouts

• 成果类型: Article
• 署名作者: Lu, Jingyi; Quevedo, Daniel E. E.; Gupta, Vijay; Dey, Subhrakanti
• 署名单位: East China University of Science & Technology; Queensland University of Technology (QUT); University of Notre Dame; Maynooth University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3131434
• 发表日期: 2023
• 页码: 31-46
• 关键词: bilevel programming; Constrained Markov decision process; remote state estimation; stealthy attack; system security and privacy

摘要

We study the maximum information gain that an adversary may obtain through hacking without being detected. Consider a dynamical process observed by a sensor that transmits a local estimate of the system state to a remote estimator according to some reference transmission policy across a packet-dropping wireless channel equipped with acknowledgments (ACK). An adversary overhears the transmissions and proactively hijacks the sensor to reprogram its transmission policy. We define perfect secrecy as keeping the averaged expected error covariance bounded at the legitimate estimator and unbounded at the adversary. By analyzing the stationary distribution of the expected error covariance, we show that perfect secrecy can be attained for unstable systems only if the ACK channel has no packet dropouts. In other situations, we prove that independent of the reference policy and the detection methods, perfect secrecy is not attainable. For this scenario, we devise a Stackelberg game to derive the optimal defensive reference policy for the legitimate estimator and present a branch-and-bound algorithm with global optimality to solve the proposed game.