Multi-model resilient observer under false data injection attacks

Olugbenga Moses Anubi, Charalambos Konstantinou, Carlos A. Wong, Satish Vedula

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, we present the concept of boosting the resiliency of optimization-based observers for cyber-physical systems (CPS) using auxiliary sources of information. Due to the tight coupling of physics, communication and computation, a malicious agent can exploit multiple inherent vulnerabilities in order to inject stealthy signals into the measurement process. The problem setting considers the scenario in which an attacker strategically corrupts portions of the data in order to force wrong state estimates which could have catastrophic consequences. The goal of the proposed observer is to compute the true states in-spite of the adversarial corruption. In the formulation, we use a measurement prior distribution generated by the auxiliary model to refine the feasible region of a traditional compressive sensing-based regression problem. A constrained optimization-based observer is developed using l_{1-minimization scheme. Numerical experiments show that the solution of the resulting problem recovers the true states of the system. The developed algorithm is evaluated through a numerical simulation example of the IEEE 14-bus system.
Original languageEnglish (US)
Title of host publicationCCTA 2020 - 4th IEEE Conference on Control Technology and Applications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages821-826
Number of pages6
ISBN (Print)9781728171401
DOIs
StatePublished - Aug 1 2020
Externally publishedYes

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