A Highly Discriminative Detector Against False Data Injection Attacks in AC State Estimation

Gang Cheng, Yuzhang Lin, Junbo Zhao, Jun Yan

Research output: Contribution to journalArticlepeer-review


False data injection attacks (FDIAs) can bypass conventional bad data detection methods. Recently developed FDIA detection methods based on statistical consistency of measurement values may not work effectively when false data do not significantly deviate from historical trends. They may also mistakenly treat actual power grid events as FDIAs. In this paper, a highly discriminative FDIA detector named the k-smallest residual similarity (k SRS) test is proposed. The method is based on the rationale that perfect FDIAs can hardly be achieved in AC state estimation, and real-world imperfect FDIAs always lead to subtle changes in the probability distributions of measurement residuals. Therefore, the statistical consistency of measurement residuals can be carefully portrayed to detect practical FDIAs in AC state estimation. Herein, the Jensen-Shannon distance (JSD) is used to precisely quantify the similarity of measurement residual distributions. Simulations on the IEEE 30-bus system demonstrate that the proposed method can achieve high detection rates and low false alarm rates under a variety of conditions where existing methods do not yield satisfactory results.

Original languageEnglish (US)
Pages (from-to)2318-2330
Number of pages13
JournalIEEE Transactions on Smart Grid
Issue number3
StatePublished - May 1 2022


  • Cyber attacks
  • false data injection attacks
  • hypothesis testing
  • measurement residuals
  • state estimation

ASJC Scopus subject areas

  • General Computer Science


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