强化学习
计算机科学
信息物理系统
计算机安全
国家(计算机科学)
重放攻击
控制(管理)
网络安全
人工智能
算法
散列函数
操作系统
作者
Yan Yu,Wen Yang,Wenjie Ding,Jiayu Zhou
标识
DOI:10.1109/tifs.2023.3268532
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.
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