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Multi-Model Resilient Observer under False Data Injection Attacks

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Document pages: 8 pages

Abstract: In this paper, we present the concept of boosting the resiliency ofoptimization-based observers for cyber-physical systems (CPS) using auxiliarysources of information. Due to the tight coupling of physics, communication andcomputation, a malicious agent can exploit multiple inherent vulnerabilities inorder to inject stealthy signals into the measurement process. The problemsetting considers the scenario in which an attacker strategically corruptsportions of the data in order to force wrong state estimates which could havecatastrophic consequences. The goal of the proposed observer is to compute thetrue states in-spite of the adversarial corruption. In the formulation, we usea measurement prior distribution generated by the auxiliary model to refine thefeasible region of a traditional compressive sensing-based regression problem.A constrained optimization-based observer is developed using l1-minimizationscheme. Numerical experiments show that the solution of the resulting problemrecovers the true states of the system. The developed algorithm is evaluatedthrough a numerical simulation example of the IEEE 14-bus system.

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