Visible to the public Multi-Model Resilient Observer under False Data Injection Attacks

TitleMulti-Model Resilient Observer under False Data Injection Attacks
Publication TypeConference Paper
Year of Publication2020
AuthorsAnubi, Olugbenga Moses, Konstantinou, Charalambos, Wong, Carlos A., Vedula, Satish
Conference Name2020 IEEE Conference on Control Technology and Applications (CCTA)
Keywordscomposability, CPS modeling, CPS Modeling and Simulation, Cyber-physical systems, Decoding, false data injection attacks, Monitoring, observer, Observers, Optimization, Predictive Metrics, pubcrawl, resilience, Resiliency, Sensors, simulation

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.

Citation Keyanubi_multi-model_2020