On false data injection attacks against Kalman filtering in power system dynamic state estimation



State estimation is a very critical component in smart grid, a typical energy-based cyber-physical system. Kalman filter has been widely used in the dynamic state estimation of power systems. Although a large number of research efforts have been made on the robustness and filtering effectiveness, little effort has been conducted on cyber attacks against Kalman filtering. To address this issue, in this paper we systematically compare three representative Kalman filtering techniques and formalize the problem of anomaly detection against false data injection attacks in Kalman filter. On the basis of our modeling results, we investigate five novel attack approaches that can bypass the anomaly detection. To defend against those attacks, we develop two countermeasures: the enhancement of Kalman filtering and the temporal-based detection algorithm. We conduct extensive performance evaluation and our data validates our theoretical finding well. Copyright © 2013 John Wiley & Sons, Ltd.