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Issue No. 03 - March (2014 vol. 25)
ISSN: 1045-9219
pp: 717-729
Qingyu Yang , SKLMSE Lab., Xi'an Jiaotong Univ., Xi'an, China
Jie Yang , SKLMSE Lab., Xi'an Jiaotong Univ., Xi'an, China
Wei Yu , Dept. of Comput. & Inf. Sci., Towson Univ., Towson, MD, USA
Dou An , SKLMSE Lab., Xi'an Jiaotong Univ., Xi'an, China
Nan Zhang , Dept. of Comput. Sci., George Washington Univ., Washington, DC, USA
Wei Zhao , Dept. of Comput. Sci., Univ. of Macau, Macau, China
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 studies show 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: 1) how can an adversary choose the meters to compromise to cause the most significant deviation of the system state estimation, and 2) how can a system operator defend against such attacks? To address these issues, we first study the problem of finding the optimal attack strategy--i.e., a data-injection attacking strategy that 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 bus systems, 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, respectively. For the protection-based defense, we identify and protect critical sensors and make the system more resilient to attacks. For the detection-based defense, we develop the spatial-based and temporal-based detection schemes to accurately identify data-injection attacks.
Vectors, Power grids, State estimation, Sensors, Transmission line measurements, Power measurement

Qingyu Yang, Jie Yang, Wei Yu, Dou An, Nan Zhang and Wei Zhao, "On False Data-Injection Attacks against Power System State Estimation: Modeling and Countermeasures," in IEEE Transactions on Parallel & Distributed Systems, vol. 25, no. 3, pp. 717-729, 2014.
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