文献类型：期刊文献

英文题名：Reinforcement Learning Solution for Cyber-Physical Systems Security Against Replay Attacks

作者：Yu, Yan[1];Yang, Wen[1];Ding, Wenjie[1];Zhou, Jiayu[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China

年份：2023

卷号：18

起止页码：2583

外文期刊名：IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY

收录：;EI(收录号：20231714016397);WOS:【SCI-EXPANDED(收录号:WOS:000981216600006)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62122026 and Grant 61973123, in part by the Program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, in part by the Shuguang Program through the Shanghai Education Development Foundation and the Shanghai Municipal Education Commission, and in part by the Special Project of Military Civilian Integration Development in Shanghai under Grant 2019-jmrh1-kj25.

语种：英文

外文关键词：Reinforcement learning; Detectors; Games; Watermarking; Technological innovation; State estimation; Noise measurement; Cyber-physical systems; distributed Kalman filtering; replay attack; reinforcement learning

摘要：The security problem of state estimation plays a critical role in monitoring and managing operation of cyber-physical systems (CPS). This paper considers the problem of network security under replay attacks and formulates a novel attack detection method. More specifically, we design a model-free reinforcement learning-based replay attack detection framework that can automatically learn and recognize the evolving attacks with more effectiveness. Attackers in some situations are more like intelligent agents with initiative, who can transform their attack strategies purposefully according to the actions of defenders. Thus, we propose a new defense strategy against the interaction between the attacker and the defender which is solved by optimization learning. The proposed analytical procedure concerning reinforcement learning technology can also be extended to the study of other control applications. Finally, the numerical examples are provided to illustrate the effectiveness of the detection method.