Deception attacks on Kalman filtering with interval estimation performance loss. Issue 35 (2022)

• Record Type: Journal Article
• Authors: Zhou, Jing; Shang, Jun; Chen, Tongwen
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 35(2022)
• Page Start: 7
• Page End: 12
• Publication Date: 2022
• Subjects: Deception attacks -- remote state estimation -- interval performance; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.11.282
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper studies the problem of optimal deception attacks against remote state estimation, where the measurement data is transmitted through an unreliable wireless channel. A malicious agent is capable of intercepting and modifying raw data, with the goal to cause the maximum estimation quality degradation and deceive χ 2 anomaly detectors. Contrary to existing studies that focused on greedy attack performance, we consider a more general scenario that the attacker aims to maximize the summation of estimation errors in a fixed interval. It is shown that the information-based optimal attack is a linear combination of the minimum mean-square error estimates of all historical prediction errors. The combination coefficients can be obtained by solving a convex optimization problem. Moreover, the proposed attack can be generalized to deceive interval χ 2 detectors with different lengths by slightly modifying the stealthiness constraint. The effectiveness of the proposed method is verified with numerical examples and comparative studies.