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

13 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

Deep learning
cyber-attacks
distributed state estimation
smart grids

Access to Document

10.1109/ACCESS.2022.3151907

Cite this

@article{5e839c329b494f3992652e0402544b96,

title = "Cyber-Physical Attack Conduction and Detection in Decentralized Power Systems",

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.",

keywords = "Deep learning, cyber-attacks, distributed state estimation, smart grids",

author = "Mostafa Mohammadpourfard and Yang Weng and Abdullah Khalili and Istemihan Genc and Alireza Shefaei and Behnam Mohammadi-Ivatloo",

note = "Publisher Copyright: {\textcopyright} 2013 IEEE.",

year = "2022",

doi = "10.1109/ACCESS.2022.3151907",

language = "English",

volume = "10",

pages = "29277--29286",

journal = "IEEE Access",

issn = "2169-3536",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Cyber-Physical Attack Conduction and Detection in Decentralized Power Systems

AU - Mohammadpourfard, Mostafa

AU - Weng, Yang

AU - Khalili, Abdullah

AU - Genc, Istemihan

AU - Shefaei, Alireza

AU - Mohammadi-Ivatloo, Behnam

PY - 2022

Y1 - 2022

N2 - 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.

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.

KW - Deep learning

KW - cyber-attacks

KW - distributed state estimation

KW - smart grids

UR - https://www.scopus.com/pages/publications/85124817182

U2 - 10.1109/ACCESS.2022.3151907

DO - 10.1109/ACCESS.2022.3151907

M3 - Article

AN - SCOPUS:85124817182

SN - 2169-3536

VL - 10

SP - 29277

EP - 29286

JO - IEEE Access

JF - IEEE Access

ER -

Cyber-Physical Attack Conduction and Detection in Decentralized Power Systems

Abstract

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this