SAI: A Suspicion Assessment-Based Inspection Algorithm to Detect Malicious Users in Smart Grid
文献类型：期刊论文
| 作者 | Liang W(梁炜)3 ; Xia XF(夏小芳)1,2,3,4; Xiao Y(肖杨)1
|
| 刊名 | IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
 |
| 出版日期 | 2020 |
| 卷号 | 15页码:361-374 |
| 关键词 | Smart grid electricity theft suspicion assessment malicious meter inspection security |
| ISSN号 | 1556-6013 |
| 产权排序 | 1 |
| 通讯作者 | Xiao, Yang(yangxiao@cs.ua.edu) ; Liang, Wei(weiliang@sia.cn) |
| 英文摘要 | 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. |
| WOS关键词 | NEIGHBORHOOD AREA NETWORKS ; REMOTE DETECTION METHOD ; ENERGY THEFT DETECTION ; METER INSPECTION ; SECURITY ; ISSUES ; HOME |
| 资助项目 | 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] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000487189200009 |
| 资助机构 | 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] |
| 源URL | [http://ir.sia.cn/handle/173321/25657]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 通讯作者 | Liang W(梁炜); Xiao Y(肖杨) |
| 作者单位 | 1.Department of Computer Science, The University of Alabama, Tuscaloosa, AL 35487-0290 USA 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 3.Key Laboratory of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.School of Computer Science and Technology, Xidian University, Xi’an 710071, China |
| 推荐引用方式 GB/T 7714 | Liang W,Xia XF,Xiao Y. 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 | Liang W,Xia XF,&Xiao Y.(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 | Liang W,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. |
入库方式： OAI收割
来源：沈阳自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。