Secure Estimation based Kalman Filter for Cyber-Physical Systems against Adversarial Attacks
For researchers and practitioners in cyber-physical systems security, this work addresses the realistic scenario of changing attack patterns, though the extension is incremental over prior fixed-attack methods.
This paper extends secure estimation for cyber-physical systems to handle time-varying sets of attacked nodes, formulating the problem as error correction and guaranteeing accurate decoding under certain conditions. Simulations on an unmanned aerial vehicle demonstrate improved performance when combining the proposed secure estimator with a Kalman filter.
Cyber-physical systems are found in many applications such as power networks, manufacturing processes, and air and ground transportation systems. Maintaining security of these systems under cyber attacks is an important and challenging task, since these attacks can be erratic and thus difficult to model. Secure estimation problems study how to estimate the true system states when measurements are corrupted and/or control inputs are compromised by attackers. The authors in [1] proposed a secure estimation method when the set of attacked nodes (sensors, controllers) is fixed. In this paper, we extend these results to scenarios in which the set of attacked nodes can change over time. We formulate this secure estimation problem into the classical error correction problem [2] and we show that accurate decoding can be guaranteed under a certain condition. Furthermore, we propose a combined secure estimation method with our proposed secure estimator and the Kalman Filter for improved practical performance. Finally, we demonstrate the performance of our method through simulations of two scenarios where an unmanned aerial vehicle is under adversarial attack.