Detection of False Data Injection Attacks in smart grids based on cubature Kalman Filtering

doi:10.1109/CCDC52312.2021.9601667

	Detection of False Data Injection Attacks in smart grids based on cubature Kalman Filtering
	Wang, Zhiwen1,2,4 ; Zhang, Qi1 ; Sun, Hongtao 3; Hu, Jiqiang 1
	2021
会议名称	33rd Chinese Control and Decision Conference (CCDC)
会议录名称	PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021)
页码	2526-2532
会议日期	MAY 22-24, 2021
会议地点	Kunming, PEOPLES R CHINA
会议录编者/会议主办者	NE Univ,Tech Comm Control & Decis Cyber Phys Syst,Chinese Assoc Automat,Kunming Univ Sci & Technol,IEEE Control Syst Soc,Tech Comm Control Theory,Chinese Assoc Automat,State Key Lab Synthet Automat Proc Ind
出版者	IEEE
摘要	The false data injection attacks (FDIAs) in smart grids can offset the power measurement data and it can bypass the traditional bad data detection mechanism. To solve this problem, a new detection mechanism called cosine similarity ratio which is based on the dynamic estimation algorithm of square root cubature Kalman filter (SRCKF) is proposed in this paper. That is, the detection basis is the change of the cosine similarity between the actual measurement and the predictive measurement before and after the attack. When the system is suddenly attacked, the actual measurement will have an abrupt change. However, the predictive measurement will not vary promptly with it owing to the delay of Kalman filter estimation. Consequently, the cosine similarity between the two at this moment has undergone a change. This causes the ratio of the cosine similarity at this moment and that at the initial moment to fluctuate considerably compared to safe operation. If the detection threshold is triggered, the system will be judged to be under attack. Finally, the standard IEEE-14bus test system is used for simulation experiments to verify the effectiveness of the proposed detection method.
关键词	Smart grids False Data Injection Attacks Attack detection Square root cubature Kalman filter Cosine similarity
DOI	10.1109/CCDC52312.2021.9601667
收录类别	CPCI-S
语种	英语
WOS研究方向	Automation & Control Systems
WOS类目	Automation & Control Systems
WOS记录号	WOS:000824370102121
ISSN	1948-9439
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	会议论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/159875
专题	电气工程与信息工程学院法学院
通讯作者	Wang, Zhiwen
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China; 2.Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Proc, Lanzhou 730050, Peoples R China; 3.Qufu Normal Univ, Coll Engn, Qufu 273100, Shandong, Peoples R China; 4.Lanzhou Univ Technol, Natl Demonstrat Ctr Expt Elect & Control Engn Edu, Lanzhou 730050, Peoples R China
第一作者单位	电气工程与信息工程学院; 兰州理工大学
通讯作者单位	电气工程与信息工程学院; 兰州理工大学
推荐引用方式 GB/T 7714	Wang, Zhiwen,Zhang, Qi,Sun, Hongtao,et al. Detection of False Data Injection Attacks in smart grids based on cubature Kalman Filtering[C]//NE Univ,Tech Comm Control & Decis Cyber Phys Syst,Chinese Assoc Automat,Kunming Univ Sci & Technol,IEEE Control Syst Soc,Tech Comm Control Theory,Chinese Assoc Automat,State Key Lab Synthet Automat Proc Ind:IEEE,2021:2526-2532.