文献类型：期刊文献

英文题名：Learning-based DoS attack game strategy over multi-process systems

作者：Hang, Zhiqiang[1];Wang, Xiaolin[1,2];Li, Fangfei[1];Ren, Yi-ang[1];Li, Haitao[3]

机构：[1]East China Univ Sci & Technol, Sch Math, Shanghai 200237, Peoples R China;[2]Minist Educ, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China;[3]Shandong Normal Univ, Sch Math & Stat, Jinan 250014, Peoples R China

年份：2024

卷号：4

期号：4

起止页码：424

外文期刊名：MATHEMATICAL MODELLING AND CONTROL

收录：WOS:【ESCI(收录号:WOS:001385230100001)】；

基金：This work is supported in part by the National Natural Science Foundation of China (Grants 62173142, 62303185, and 62073202) , in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, in part by the Shanghai Sailing Program under Grant 23YF1409500, and in part by the Fundamental Research Funds for the Central Universities under Grant JKM01231838.

语种：英文

外文关键词：cyber-physical systems; game theory; DoS attack; AoI; multi-agent deep deterministic policy gradient

摘要：In cyber-physical systems, the state information from multiple processes is sent simultaneously to remote estimators through wireless channels. However, with the introduction of open media such as wireless networks, cyber-physical systems may become vulnerable to denial-of-service attacks, which can pose significant security risks and challenges to the systems. To better understand the impact of denial-of-service attacks on cyber-physical systems and develop corresponding defense strategies, several research papers have explored this issue from various perspectives. However, most current works still face three limitations. First, they only study the optimal strategy from the perspective of one side (either the attacker or defender). Second, these works assume that the attacker possesses complete knowledge of the system's dynamic information. Finally, the power exerted by both the attacker and defender is assumed to be small and discrete. All these limitations are relatively strict and not suitable for practical applications. In this paper, we addressed these limitations by establishing a continuous power game problem of a denial-of-service attack in a multi-process cyber-physical system with asymmetric information. We also introduced the concept of the age of information to comprehensively characterize data freshness. To solve this problem, we employed the multi-agent deep deterministic policy gradient algorithm. Numerical experiments demonstrate that the algorithm is effective for solving the game problem and exhibits convergence in multi-agent environments, outperforming other algorithms.