Robust and resilient estimation for Cyber-Physical Systems under adversarial attacks

In this paper, we propose a novel state estimation algorithm that is resilient to sparse data injection attacks and robust to additive and multiplicative modeling errors. By leveraging principles of robust optimization, we construct uncertainty sets that lead to tractable optimization solutions. As a corollary, we obtain a novel robust filtering algorithm when there are no attacks, which can be viewed as a “frequentist” robust estimator as no known priors are assumed. We also describe the use of cross-validation to determine the hyperparameters of our estimator. The effectiveness of our estimator is demonstrated in simulations of an IEEE 14-bus electric power system.