On false data-injection attacks against power system state estimation: Modeling and countermeasures

Qingyu Yang, Jie Yang, Wei Yu, Dou An, Nan Zhang, Wei Zhao

Research output: Contribution to journalArticlepeer-review

235 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number6490324
Pages (from-to)717-729
Number of pages13
JournalIEEE Transactions on Parallel and Distributed Systems
Volume25
Issue number3
DOIs
StatePublished - Mar 2014

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics

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