文献类型：期刊文献

英文题名：Security analysis and defense strategy of distributed filtering under false data injection attacks

作者：Zhou, Jiayu[1];Yang, Wen[1];Zhang, Heng[2];Zheng, Wei Xing[3];Xu, Yong[4];Tang, Yang[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China;[2]Jiangsu Ocean Univ, Sch Comp Engn, Jiangsu 222005, Peoples R China;[3]Western Sydney Univ, Sch Comp Data & Math Sci, Sydney, NSW 2751, Australia;[4]Guangdong Univ Technol, Guangdong Prov Key Lab Intelligent Decis & Cooper, Guangzhou 510006, Peoples R China

年份：2022

卷号：138

外文期刊名：AUTOMATICA

收录：;EI(收录号：20220511543298);WOS:【SCI-EXPANDED(收录号:WOS:000788851300024)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grants (62122026, 61973123, 61873106, 62121004), in part by the Natural Science Foundation of Jiangsu Province for Distinguished Young Scholars, China under Grant BK20200049, in part by the NSW Cyber Security Network in Australia under Grant (P00025091), the projects sponsored by the Programme of Introducing Talents of Discipline to Universities (the 111 Project), China under Grant B17017, Shuguang Program supported by Shanghai Education Development Foundation, China and Shanghai Municipal Education Commission, China, the Fundamental Research Funds for the Central Universities, China.

语种：英文

外文关键词：Distributed estimation; False data injection attacks; Security analysis; Protection strategy

摘要：This paper investigates the distributed state estimation for multi-sensor networks under false data injection attacks. The well-known chi(2) detector is first considered for detecting the authenticity of the transmitted data. A necessary and sufficient condition for the insecurity of the distributed estimation system is derived under which the hostile attacks can bypass the false data detector and degrade the estimation performance. Moreover, an algorithm for generating false data is provided to keep the attack stealthy. In order to overcome the detection vulnerability, a new protection strategy is proposed to ensure that the distributed estimator is secure under false data injection attacks. It is worth emphasizing that the strategy adopts a stochastic rule instead of a fixed threshold to detect suspicious data, which effectively avoids the occurrence of the truncated Gaussian distribution. A simulation example of moving vehicle is presented to demonstrate the effectiveness of the developed approaches. (C)& nbsp;2022 Elsevier Ltd. All rights reserved.