Multi-Model Resilient Observer under False Data Injection Attacks

TL;DR

This paper proposes a resilient observer for cyber-physical systems that leverages auxiliary information and compressive sensing techniques to accurately estimate system states despite false data injection attacks.

Contribution

It introduces a novel optimization-based observer that integrates auxiliary models and l1-minimization to enhance resilience against malicious data corruption.

Findings

The observer successfully recovers true system states under attack.

Numerical simulations demonstrate improved robustness over traditional methods.

The approach is validated on the IEEE 14-bus system model.

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 l1-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.