详细信息
A Secure Encoding Mechanism Against Deception Attacks on Multisensor Remote State Estimation ( SCI-EXPANDED收录 EI收录)
文献类型:期刊文献
英文题名:A Secure Encoding Mechanism Against Deception Attacks on Multisensor Remote State Estimation
作者:Zhou, Jiayu[1];Ding, Wenjie[1];Yang, Wen[1]
机构:[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Shanghai 200237, Peoples R China
年份:2022
卷号:17
起止页码:1959
外文期刊名:IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
收录:;EI(收录号:20222112149894);WOS:【SCI-EXPANDED(收录号:WOS:000800168400001)】;
基金:This work was supported in part by the National Natural Science Foundation of China under Grant 62122026 and Grant 61973123, in part by the projects sponsored by the Program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, in part by the Shuguang Program supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission, and in part by the Fundamental Research Funds for the Central Universities. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Stefano Tomasin.
语种:英文
外文关键词:Encoding; Detectors; Security; Technological innovation; State estimation; Wireless networks; Manufacturing; State estimation; security; encoding; deception attack; false data detector
摘要:This paper studies the defense strategy of remote state estimation under deception attacks. In order to prevent the stealthy attacker from reducing the estimation performance without triggering an alarm, an encoding-decoding mechanism combining linear transformation and artificial noise is proposed. Moreover, the detection performance under three different attack scenarios is analyzed. It is proved that the false data detector can effectively identify the attack or weaken its impact on the system under the proposed strategy, so as to ensure the security of the system. From the perspective of an attacker, an algorithm that can deduce the approximate values of the encoding parameters is also provided, which reveals how the magnitude of the artificial noise affects the accuracy of the attacker's inference. Finally, a simulation example is presented to verify the effectiveness of the developed approach.
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