Worst-case stealthy innovation-based linear attack on remote state estimation. (March 2018)

• Record Type: Journal Article
• Authors: Guo, Ziyang; Shi, Dawei; Johansson, Karl Henrik; Shi, Ling
• Is Part Of: Automatica. Volume 89(2018)
• Page Start: 117
• Page End: 124
• Publication Date: 2018-03
• Subjects: Integrity attack -- Kullback–Leibler divergence -- Remote state estimation -- Cyber–Physical system security; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2017.11.018
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: In this work, a security problem in cyber–physical systems is studied. We consider a remote state estimation scenario where a sensor transmits its measurement to a remote estimator through a wireless communication network. The Kullback–Leibler divergence is adopted as a stealthiness metric to detect system anomalies. We propose an innovation-based linear attack strategy and derive the remote estimation error covariance recursion in the presence of attack, based on which a two-stage optimization problem is formulated to investigate the worst-case attack policy. It is proved that the worst-case attack policy is zero-mean Gaussian distributed and the numerical solution is obtained by semi-definite programming. Moreover, an explicit algorithm is provided to calculate the compromised measurement. The trade-off between attack stealthiness and system performance degradation is evaluated via simulation examples.