On Information Fusion in Optimal Linear FDI Attacks Against Remote State Estimation

被引:6
|
作者
Zhou, Jing [1 ]
Shang, Jun [1 ]
Chen, Tongwen [1 ]
机构
[1] Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6G 1H9, Canada
来源
基金
加拿大自然科学与工程研究理事会;
关键词
Kalman filtering; remote state estimation; side information; False data injection (FDI) attacks; DATA-INJECTION ATTACKS; SYSTEMS; INPUT;
D O I
10.1109/TCNS.2023.3260041
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article studies the problem of false data injection (FDI) attacks against remote state estimation. The scenario that malicious attackers can intercept original data packets and also eavesdrop on some side information of system states with extra sensors is considered. To clarify a counterintuitive issue in existing work, a different innovation-based linear attack policy fusing all available information is proposed. First, the evolution of a posteriori estimation error covariance with FDI attacks is derived. Then, explicit solutions of optimal stealthy attack coefficients are obtained without solving optimization problems numerically. The condition under which there exist multiple optimal attacks is analyzed. In addition, an easy-to-check criterion for comparing two information fusion methods in scalar systems is given. Simulation results show that, compared with existing work, the proposed attack strategy can completely deceive the anomaly detector and cause more severe performance degradation in remote state estimation.
引用
收藏
页码:2085 / 2096
页数:12
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