DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2013.92
Qingyu Yang , Xi'an Jiaotong University, Xi'an
Jie Yang , Xi'an Jiaotong University, Xi'an
Wei Yu , Towson University, Towson
Dou An , Xi'an Jiaotong University, Xi'an
Nan Zhang , The George Washington University, Washington
Wei Zhao , University of Macau, Macau
It is critical for a power system to estimate its operation state based on meter measurements in the field and the configuration of power grid networks. Recent study shows that the adversary can bypass the existing bad data detection schemes, posing dangerous threats to the operation of power grid systems. Nevertheless, two critical issues remain open: (i) how can an adversary choose the meters to compromise in order to cause the most significant deviation of the system state estimation, and (ii) how can a system operator defend against such attacks? To address these issues, we study the problem of finding the optimal attack strategy - i.e., a data-injection attacking strategy which selects a set of meters to manipulate so as to cause the maximum damage. We formalize the problem and develop efficient algorithms to identify the optimal meter set. We implement and test our attack strategy on various IEEE standard, and demonstrate its superiority over a baseline strategy of random selections. To defend against false data injection attacks, we propose a protection-based defense and a detection-based defense. For the detection-based defense, we develop the spatial-based and temporal-based detection schemes to accurately identify data injection attacks. Our experimental data show that our spatial-based detection algorithm can detect at least 95% attacks when the adversary changes up to 6% of the magnitude values of state variables. Our temporal-based detection algorithm can identify compromised meters quickly.
Cyber security, Cyber-physical systems, Power grid, State estimation
N. Zhang, D. An, W. Yu, J. Yang, Q. Yang and W. Zhao, "On False Data Injection Attacks Against Power System State Estimation: Modeling and Countermeasures," in IEEE Transactions on Parallel & Distributed Systems.