Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid

doi:10.1109/TCNS.2022.3141026

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid
	Liu, Yifa 1,2; Cheng, Long 1,2
刊名	IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
	2022-09-01
卷号	9 期号:3 页码:1238-1250
关键词	Attack sequence false data injection Kalman filter smart grid security state estimation
ISSN号	2325-5870
DOI	10.1109/TCNS.2022.3141026
通讯作者	Cheng, Long(long.cheng@ia.ac.cn)
英文摘要	As one of the most dangerous cyber attacks in smart grids, the false data injection attacks pose a serious threat to power system security. To detect the false data, the traditional residual method and other improved methods, such as the Kalman-filter-based detector, have been proposed. However, these methods often have defects, especially in a very complex networked system with noises. By investigating the tolerance to the uncertainty in the residual detection method and properties of noises, the attack magnitude planning has been presented to hide the attack behind noises, which can bypass the residual detection method. As to the Kalman-filter-based detector, this article designs a specific attack strategy that can successfully deceive the Kalman-filter-based detector. Under this strategy, the false data injected at each step are used to balance the anomalies caused by previous false data, making the system look quite normal in monitoring, while deviating the system from normal operation eventually.
资助项目	National Natural Science Foundation of China[61633016] ; National Natural Science Foundation of China[61873268] ; National Natural Science Foundation of China[62025307] ; National Natural Science Foundation of China[U1913209] ; Beijing Natural Science Foundation[JQ19020]
WOS关键词	SYSTEMS
WOS研究方向	Automation & Control Systems ; Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000856122100019
资助机构	National Natural Science Foundation of China ; Beijing Natural Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50128]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者	Cheng, Long
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Liu, Yifa,Cheng, Long. Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid[J]. IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS,2022,9(3):1238-1250.
APA	Liu, Yifa,&Cheng, Long.(2022).Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid.IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS,9(3),1238-1250.
MLA	Liu, Yifa,et al."Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid".IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 9.3(2022):1238-1250.