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A Survey on the Detection Algorithms for False Data Injection Attacks in Smart Grids

  • Ahmed S. Musleh
  • , Guo Chen*
  • , Zhao Yang Dong
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

Cyber-physical attacks are the main substantial threats facing the utilization and development of the various smart grid technologies. Among these attacks, false data injection attack represents a main category with its widely varied types and impacts that have been extensively reported recently. In addressing this threat, several detection algorithms have been developed in the last few years. These were either model-based or data-driven algorithms. This paper provides an intensive summary of these algorithms by categorizing them and elaborating on the pros and cons of each category. The paper starts by introducing the various cyber-physical attacks along with the main reported incidents in history. The significance and the impacts of the false data injection attacks are then reported. The concluding remarks present the main criteria that should be considered in developing future detection algorithms for the false data injection attacks. © 2019 IEEE.
Original languageEnglish
Pages (from-to)2218-2234
JournalIEEE Transactions on Smart Grid
Volume11
Issue number3
Online published30 Oct 2019
DOIs
Publication statusPublished - May 2020
Externally publishedYes

Research Keywords

  • Cyber-physical attacks
  • data-driven detection algorithms
  • false data injection
  • machine learning
  • model-based detection algorithms
  • smart grid
  • state estimation
  • stealth attacks

Policy Impact

  • Cited in Policy Documents

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