Cyber-Physical Attack Conduction and Detection in Decentralized Power Systems

Mostafa Mohammadpourfard, Yang Weng, Abdullah Khalili, Istemihan Genc, Alireza Shefaei, Behnam Mohammadi-Ivatloo

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The expansion of power systems over large geographical areas renders centralized processing inefficient. Therefore, the distributed operation is increasingly adopted. This work introduces a new type of attack against distributed state estimation of power systems, which operates on inter-area boundary buses. We show that the developed attack can circumvent existing robust state estimators and the convergence-based detection approaches. Afterward, we carefully design a deep learning-based cyber-anomaly detection mechanism to detect such attacks. Simulations conducted on the IEEE 14-bus system reveal that the developed framework can obtain a very high detection accuracy. Moreover, experimental results indicate that the proposed detector surpasses current machine learning-based detection methods.

Original languageEnglish (US)
Pages (from-to)29277-29286
Number of pages10
JournalIEEE Access
StatePublished - 2022


  • Deep learning
  • cyber-attacks
  • distributed state estimation
  • smart grids

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering


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