Review on Artificial Intelligence-based Network Attack Detection in Power Systems

被引：0
|
作者：
Zhang B. [1 ]
Liu X. [1 ]
Yu Z. [1 ]
Wang W. [1 ]
Jin Q. [2 ]
Li W. [2 ]
机构：
[1] College of Electrical and Information Engineering, Hunan University, Changsha
[2] Nanjing NARI Information & Communication Technology Co., Ltd., Nanjing
来源：
Gaodianya Jishu/High Voltage Engineering | 2022年 / 48卷 / 11期
关键词：
active defense; artificial intelligence; attack detection; network attacks; new power systems;
D O I：
10.13336/j.1003-6520.hve.20220300
中图分类号：
学科分类号：
摘要：
With the deep integration of cyber domain and physical domain and the fast development of new power systems, cyber attacks pose a severe threat to the safe and reliable operation of power systems, thus it is essential to develop detection methods for cyber attacks. Artificial Intelligence (AI) is recognized as a popular method to detect cyber attacks because of its advantages in extracting data characteristic, modeling complex systems and solving nonlinear systems. This paper first investigates three characteristics of structure complicacy, cyber physical coupling and intelligence in new power systems, and reveals the possible cyber threats that new power systems might encounter in the physical, network and application layers. After that, the AI-based detection methods for cyber attacks in new power systems are reviewed from the perspectives of terminal devices of the physical layer, network layer traffic, packets and business systems in the application layer. Finally, the coupling relationships among attack detection, attack blocking and ex-post recovery are studied, some key technologies of active defense to cyber attacks are discussed, and the corresponding future work are also given. © 2022 Science Press. All rights reserved.
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页码：4413 / 4426
页数：13
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共 92 条
  • [1] XIN Baoan, SHAN Baoguo, LI Qionghui, Et al., Rethinking of the “three elements of energy” toward carbon peak and carbon neutrality, Proceedings of the CSEE, 42, 9, pp. 3117-3125, (2022)
  • [2] HUANG Yuhan, DING Tao, LI Yuting, Et al., Decarbonization technologies and inspirations for the development of novel power systems in the context of carbon neutrality, Proceedings of the CSEE, 41, pp. 28-51, (2021)
  • [3] KANG Chongqing, YAO Liangzhong, Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy, Automation of Electric Power Systems, 41, 9, pp. 2-11, (2017)
  • [4] YIN X F, ZHU Y M, HU J K., A subgrid-oriented privacy-preserving microservice framework based on deep neural network for false data injection attack detection in smart grids, IEEE Transactions on Industrial Informatics, 18, 3, pp. 1957-1967, (2022)
  • [5] MUSLEH A S, GUO C, ZHAO Y D., A survey on the detection algorithms for false data injection attacks in smart grids, IEEE Transactions on Smart Grid, 11, 3, pp. 2218-2234, (2020)
  • [6] TANG Yi, CUI Han, LI Feng, Et al., Review on artificial intelligence in power system transient stability analysis, Proceedings of the CSEE, 39, 1, pp. 2-13, (2019)
  • [7] AKIMOTO Y, TANAKA H, YOSHIZAWA J, Et al., Transient stability expert system, IEEE Transactions on Power Systems, 4, 1, pp. 312-320, (1989)
  • [8] GUO Jianbo, Characteristics and development challenges of new power systems, (2021)
  • [9] YANG Ting, ZHAO Liyuan, WANG Chengshan, Review on application of artificial intelligence in power system and integrated energy system, Automation of Electric Power Systems, 43, 1, pp. 2-14, (2019)
  • [10] ANWAR A, MAHMOOD A, RAY B, Et al., Machine learning to ensure data integrity in power system topological network database, Electronics, 9, 4, (2020)
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