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A Deviation-based Detection Method against False Data Injection Attacks in Smart Grid
Pei C(裴超)1,2,3,4,5; Xiao Y(肖杨)5; Liang W(梁炜)1,2,3,6; Han XJ(韩晓佳)2,3,4,6
Department工业控制网络与系统研究室
Source PublicationIEEE Access
ISSN2169-3536
2021
Volume9Pages:15499-15509
Indexed BySCI ; EI
EI Accession number20210409806599
WOS IDWOS:000613206800001
Contribution Rank1
Funding OrganizationNational Key Research and Development Program of China under Grant 2017YFE0101300 ; National Natural Science Foundation of China under Grant 62022088 ; Liaoning Revitalization Talents Program under Grant XLYC1902110 ; Liaoning Provincial Natural Science Foundation of China under Grant 2020JH2/10500002 and Grant 2019-YQ-09 ; International Partnership Program of Chinese Academy of Sciences under Grant 173321KYSB20180020 and Grant 173321KYSB20200002 ; China Scholarship Council
KeywordState estimation false data injection attacks smart grid cyber security Kalman filter cyber physical system
Abstract

State estimation plays a vital role to ensure safe and reliable operations in smart grid. Intelligent attackers can carefully design a destructive and stealthy false data injection attack (FDIA) sequence such that commonly used weighted least squares estimator combined with residual-based detection method is vulnerable to the FDIA. To effectively defend against an FDIA, in this paper, we propose a robust deviation-based detection method, in which an additional Kalman filter is introduced while retaining the original weighted least squares estimator, so that there are two state estimators. Moreover, an exponential weighting function is also applied to the introduced Kalman filter in our proposed method. When an FDIA occurs, the estimation results of weighted least squares estimator depend only on meter measurements at each time slot, but there is an adjustment process of estimated states for the Kalman filter based on historical states' transitions. Meanwhile, based on the exponential weighting function, estimated measurements in the Kalman filter can be adaptively suppressed for different attack strengths of FDIAs, and then the difference of the results of these two estimators increases. Subsequently, FDIAs can be effectively detected by checking the deviation of estimated measurements about the two estimators with a detection threshold. Experimental results validate the effectiveness of the proposed detection method against FDIAs. The impact of different attack strengths and noise on detection performance is also evaluated and analyzed.

Language英语
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS Research AreaComputer Science ; Engineering ; Telecommunications
Funding ProjectNational Key Research and Development Program of China[2017YFE0101300] ; National Natural Science Foundation of China[62022088] ; Liaoning Revitalization Talents Program[XLYC1902110] ; Liaoning Provincial Natural Science Foundation of China[2020JH2/10500002] ; Liaoning Provincial Natural Science Foundation of China[2019-YQ-09] ; International Partnership Program of Chinese Academy of Sciences[173321KYSB20180020] ; International Partnership Program of Chinese Academy of Sciences[173321KYSB20200002] ; China Scholarship Council
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Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28204
Collection工业控制网络与系统研究室
Corresponding AuthorLiang W(梁炜)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016
2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
4.University of Chinese Academy of Sciences, Beijing 100049, China
5.Department of Computer Science, The University of Alabama, Tuscaloosa, AL 35487-0290 USA.
6.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Recommended Citation
GB/T 7714
Pei C,Xiao Y,Liang W,et al. A Deviation-based Detection Method against False Data Injection Attacks in Smart Grid[J]. IEEE Access,2021,9:15499-15509.
APA Pei C,Xiao Y,Liang W,&Han XJ.(2021).A Deviation-based Detection Method against False Data Injection Attacks in Smart Grid.IEEE Access,9,15499-15509.
MLA Pei C,et al."A Deviation-based Detection Method against False Data Injection Attacks in Smart Grid".IEEE Access 9(2021):15499-15509.
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