False Data Injection Detection for Phasor Measurement Units

Saleh Almasabi, Turki Alsuwian*, Muhammad Awais, Muhammad Irfan, Mohammed Jalalah, Belqasem Aljafari, Farid A. Harraz

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

8 Citations (Scopus)
83 Downloads (Pure)


Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances.
Original languageEnglish
Article number3146
Pages (from-to)e3146
Issue number9
Early online date20 Apr 2022
Publication statusPublished - 20 Apr 2022


  • cyber-physical security
  • false data injection attacks
  • machine learning
  • state estimation
  • phasor measurement units
  • smart grids


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