Detecting stealthy integrity attacks in a class of nonlinear cyber–physical systems: A backward-in-time approach. (July 2022)

• Record Type: Journal Article
• Authors: Zhang, Kangkang; Keliris, Christodoulos; Polycarpou, Marios M.; Parisini, Thomas
• Is Part Of: Automatica. Volume 141(2022)
• Publication Date: 2022-07
• Subjects: Stealthy attacks -- Nonlinear cyber–physical systems -- Attack detection -- Fixed-point smoother; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2022.110262
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: This paper proposes a stealthy integrity attack detection methodology for a class of nonlinear cyber–physical systems subject to disturbances. An equivalent increment of the system at a time prior to the attack occurrence time is introduced, which is theoretically proved to be effective to detect stealthy integrity attacks. A backward-in-time estimator is developed via the fixed-point smoother design tool to exploit this equivalent increment and allow the detection of the attack. More specifically, an asymptotically stable incremental system is introduced to characterize stealthy integrity attacks, and its backward-in-time solution at a fixed time prior to the attack occurrence formulates the equivalent increment. When running reversely in time, the divergence property of such an asymptotically stable incremental system enables the equivalent increment to detect stealthy integrity attacks. A fixed-point smoother is introduced to estimate the unknown equivalent increment for a class of Lipschitz nonlinear physical plants, such that the estimation error satisfies the H ∞ performance objective. Based on the equivalent increment and its estimation provided by the smoother, suitable residual and threshold signals are generated, allowing the detection of the considered stealthy integrity attacks. A detectability analysis is conducted to rigorously characterize the class of detectable attacks. Finally, a case study is presented to illustrate the effectiveness of the developed backward-in-time attack detection methodology.