Special Issue Paper
On false data injection attacks against Kalman filtering in power system dynamic state estimation
Article first published online: 27 AUG 2013
Copyright © 2013 John Wiley & Sons, Ltd.
Security and Communication Networks
How to Cite
Yang, Q., Chang, L. and Yu, W. (2013), On false data injection attacks against Kalman filtering in power system dynamic state estimation. Security Comm. Networks. doi: 10.1002/sec.835
- Article first published online: 27 AUG 2013
- Manuscript Accepted: 3 JUN 2013
- Manuscript Revised: 14 MAY 2013
- Manuscript Received: 23 DEC 2012
- smart grid;
- Kalman filter;
- dynamic state estimation;
- false data injection attack;
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.