文献类型：期刊文献

英文题名：Security Analysis of Distributed Consensus Filtering Under Replay Attacks

作者：Huang, Jiahao[1];Yang, Wen[1];Ho, Daniel W. C.[2];Li, Fangfei[3];Tang, Yang[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]City Univ Hong Kong, Dept Math, Hong Kong, Peoples R China;[3]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China

年份：2024

卷号：54

期号：6

起止页码：3526

外文期刊名：IEEE TRANSACTIONS ON CYBERNETICS

收录：;EI(收录号：20233614686865);WOS:【SCI-EXPANDED(收录号:WOS:001060566900001)】；

基金：This work was supported in part by the Natural Science Foundation of China under Grant 62233005 and Grant 62173142; in part by the National Natural Science Fund for Distinguished Young Scholars under Grant 61725301; in part by the Program of Shanghai Academic Research Leader under Grant 20XD1401300; in part by the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and Shanghai AI Lab; in part by the Research Grants Council of Hong Kong Special Administrative Region, China, under Grant CityU 11202819; and in part by the Special Project of Military Civilian Integration Development in Shanghai under Grant 2019-jmrh1-kj25.& nbsp;

语种：英文

外文关键词：Sensors; Security; Estimation error; Control systems; Covariance matrices; Detectors; Wireless sensor networks; Cyber security; cyber-physical systems (CPSs); distributed consensus filtering; replay attack

摘要：This work studies the security of consensus-based distributed filtering under the replay attack, which can freely select a part of sensors and modify their measurements into previously recorded ones. We analyze the performance degradation of distributed estimation caused by the replay attack, and utilize the Kullback-Leibler (K-L) divergence to quantify the attack stealthiness. Specifically, for a stable system, we prove that under any replay attack, the estimation error is not only bounded, but also can re-enter the steady state. In that case, we prove that the replay attack is ? -stealthy, where ? can be calculated based on two Lyapunov equations. On the other hand, for an unstable system, we prove that the trace of estimation error covariance is lower bounded by an exponential function, which indicates that the estimation error may diverge due to the attack. In view of this, we provide a sufficient condition to ensure that any replay attack is detectable. Furthermore, we analyze the case that the adversary starts to attack only if the current measurement is close to a previously recorded one. Finally, we verify the theoretical results via several numerical simulations.