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DOI:10.35833/MPCE.2021.000604
A Review on Cybersecurity Analysis, Attack Detection, and Attack Defense Methods in Cyber-physical Power Systems
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Author: Dajun Du1,Minggao Zhu1,Xue Li1,Minrui Fei1,Siqi Bu2,Lei Wu3,Kang Li4

Author Affiliation: 1.Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China;2.the Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China;3.the Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA;4.the School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK

Foundation:
The work of D. Du, M. Zhu, X. Li, and M. Fei was supported in part by the National Science Foundation of China (No. 92067106) and 111 Project (No. D18003).
Abstract: Potential malicious cyber-attacks to power systems which are connected to a wide range of stakeholders from the top to tail will impose significant societal risks and challenges. The timely detection and defense are of crucial importance for safe and reliable operation of cyber-physical power systems (CPPSs). This paper presents a comprehensive review of some of the latest attack detection and defense strategies. Firstly, the vulnerabilities brought by some new information and communication technologies (ICTs) are analyzed, and their impacts on the security of CPPSs are discussed. Various malicious cyber-attacks on cyber and physical layers are then analyzed within CPPSs framework, and their features and negative impacts are discussed. Secondly, two current mainstream attack detection methods including state estimation based and machine learning based methods are analyzed, and their benefits and drawbacks are discussed. Moreover, two current mainstream attack defense methods including active defense and passive defense methods are comprehensively discussed. Finally, the trends and challenges in attack detection and defense strategies in CPPSs are provided.

Keywords:
Cyber-physical power systems ; security threat ; attack detection ; attack defense ; state estimation ; machine learning
Received:September 02, 2021               Online Time:2023/05/23
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