| 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
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| Department | 工业控制网络与系统研究室 |
| Corresponding Author | Xiao, Yang(yangxiao@cs.ua.edu) ; Liang, Wei(weiliang@sia.cn) |
| Source Publication | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
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| ISSN | 1556-6013 |
| 2020 | |
| Volume | 15Pages:361-374 |
| Indexed By | SCI |
| WOS ID | WOS:000487189200009 |
| Contribution Rank | 1 |
| Funding Organization | National 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] |
| Keyword | Smart grid electricity theft suspicion assessment malicious meter inspection security |
| Abstract | Integrated 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 Subject | Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| WOS Keyword | NEIGHBORHOOD AREA NETWORKS ; REMOTE DETECTION METHOD ; ENERGY THEFT DETECTION ; METER INSPECTION ; SECURITY ; ISSUES ; HOME |
| WOS Research Area | Computer Science ; Engineering |
| Funding Project | National 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 |
| Identifier | http://ir.sia.cn/handle/173321/25657 |
| Collection | 工业控制网络与系统研究室 |
| Corresponding Author | Xiao Y(肖杨); Liang W(梁炜) |
| Affiliation | 1.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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| File Name/Size | DocType | Version | Access | License | ||
| SAI_ A Suspicion Ass（3069KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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