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SAI: A Suspicion Assessment-Based Inspection Algorithm to Detect Malicious Users in Smart Grid
Xia XF(夏小芳)1,2,3,4; Xiao Y(肖杨)4; Liang W(梁炜)2
Department工业控制网络与系统研究室
Corresponding AuthorXiao, Yang(yangxiao@cs.ua.edu) ; Liang, Wei(weiliang@sia.cn)
Source PublicationIEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
ISSN1556-6013
2020
Volume15Pages:361-374
Indexed BySCI
WOS IDWOS:000487189200009
Contribution Rank1
Funding OrganizationNational Key Research and Development Program of China [2017YFE0101300] ; U.S. National Science FoundationNational Science Foundation (NSF) [CNS-1059265] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [61374200, 71661147005, 61702403] ; Key Research and Development Plan of Jiangxi Province [20181ACE50029] ; National Natural Science Foundation of Shaanxi Province [2019ZDLGY13-09, 2019CGXNG-023]
KeywordSmart grid electricity theft suspicion assessment malicious meter inspection security
AbstractIntegrated with cutting-edge equipment and technologies, smart grid takes prominent advantages over traditional power systems. However, hardware and software techniques also bring smart grid numerous security concerns, especially various cyberattacks. Malicious users can launch cyberattacks to tamper with smart meters anytime and anywhere, mainly for the purpose of stealing electricity. This makes electricity theft much easier to commit and more difficult to detect. Researchers have devised many approaches to identify malicious users. However, these approaches suffer from either poor accuracy or expensive cost of deploying monitoring devices. This paper aims to locate malicious users using a limited number of monitoring devices (called inspectors) within the shortest detection time. Before inspectors conduct any inspection, suspicions that users steal electricity are comprehensively assessed, mainly through analyzing prior records of electricity theft as well as deviations between the reported and predicted normal consumptions. On the basis of these suspicions, we further propose a suspicion assessment-based inspection (SAI) algorithm, in which the users with the highest suspicions will be first probed individually. Then, the other users will be probed by a binary tree-based inspection strategy. The binary tree is built according to users' suspicions. The inspection order of the nodes on the binary tree is also determined by the suspicions. The experiment results show that the SAI algorithm outperforms the existing methods.
Language英语
WOS SubjectComputer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS KeywordNEIGHBORHOOD AREA NETWORKS ; REMOTE DETECTION METHOD ; ENERGY THEFT DETECTION ; METER INSPECTION ; SECURITY ; ISSUES ; HOME
WOS Research AreaComputer Science ; Engineering
Funding ProjectNational Key Research and Development Program of China[2017YFE0101300] ; U.S. National Science Foundation[CNS-1059265] ; National Natural Science Foundation of China[61374200] ; National Natural Science Foundation of China[71661147005] ; National Natural Science Foundation of China[61702403] ; Key Research and Development Plan of Jiangxi Province[20181ACE50029] ; National Natural Science Foundation of Shaanxi Province[2019ZDLGY13-09] ; National Natural Science Foundation of Shaanxi Province[2019CGXNG-023]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25657
Collection工业控制网络与系统研究室
Corresponding AuthorXiao Y(肖杨); Liang W(梁炜)
Affiliation1.School of Computer Science and Technology, Xidian University, Xi’an 710071, China
2.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
4.Department of Computer Science, The University of Alabama, Tuscaloosa, AL 35487-0290 USA
Recommended Citation
GB/T 7714
Xia XF,Xiao Y,Liang W. SAI: A Suspicion Assessment-Based Inspection Algorithm to Detect Malicious Users in Smart Grid[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2020,15:361-374.
APA Xia XF,Xiao Y,&Liang W.(2020).SAI: A Suspicion Assessment-Based Inspection Algorithm to Detect Malicious Users in Smart Grid.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,15,361-374.
MLA Xia XF,et al."SAI: A Suspicion Assessment-Based Inspection Algorithm to Detect Malicious Users in Smart Grid".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 15(2020):361-374.
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