文献类型：期刊文献

英文题名：Resilient Consensus-Based Distributed Filtering: Convergence Analysis Under Stealthy Attacks

作者：Huang, Jiahao[1];Tang, Yang[1];Yang, Wen[1];Li, Fangfei[2]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Math, Shanghai 200237, Peoples R China

年份：2020

卷号：16

期号：7

起止页码：4878

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20201508411314);WOS:【SCI-EXPANDED(收录号:WOS:000522523000054)】；

基金：This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFC0809302; in part by the National Natural Science Foundation of China under Grant 61751305, Grant 61673176, Grant 61973123, and Grant 61773161; in part by the Science and Technology Commission of Shanghai Municipality under Grant 18ZR1409800; and in part by the Program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017.

语种：英文

外文关键词：Cyber-physical systems (CPSs); distributed estimation; security; stealthy attack

摘要：In this article, we consider the security problem for the consensus-based distributed state estimation. To resist the malicious attacker who can falsify the data transmitted through the wireless channel, each node equips with an attack defender, which is based on the measurement of its built-in sensor. Under the stealthy attack, which can deceive the defender, we investigate the resilience and convergence of the distributed estimation in two different attack scenarios. For the attack with enough communication resources, we provide a sufficient condition of the optimal attack to quantify the maximum estimation performance degradation. We also analyze the resilience of the worst case distributed estimation caused by the attacker. For the attack with limited resources, the optimal Kalman gain for each node is derived to maximize its estimation performance under the attack. We also give a sufficient condition to guarantee the convergence of the distributed estimation in this case. Finally, numerical simulations are provided to illustrate the effect of the defender on guaranteeing the resilience of sensor networks against attacks.