Cyber-Physical Attack Conduction and Detection in Decentralized Power Systems

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

doi:10.1109/ACCESS.2022.3151907

Cyber-Physical Attack Conduction and Detection in Decentralized Power Systems

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

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	29277-29286
Number of pages	10
Journal	IEEE Access
Volume	10
DOIs	https://doi.org/10.1109/ACCESS.2022.3151907
Publication status	Published - 2022

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Funding

This work was supported by the TÜBITAK and European Commission Horizon 2020 Marie Skłodowska-Curie Actions Co-Fund Program under Project 120C080.

Funders	Funder number
European Commission Horizon 2020 Marie Skłodowska-Curie Actions	120C080
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Keywords

cyber-attacks
Deep learning
distributed state estimation
smart grids

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10.1109/ACCESS.2022.3151907

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abstract = "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.",

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AU - Shefaei, Alireza

AU - Mohammadi-Ivatloo, Behnam

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AB - 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.

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Cyber-Physical Attack Conduction and Detection in Decentralized Power Systems

Abstract

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