Detecting False Data Injection Attacks Using Canonical Variate Analysis in Power Grid | |
Pei C(裴超)1,2,3,4,5; Xiao Y(肖杨)4; Liang W(梁炜)1,2,3; Han XJ(韩晓佳)1,2,5 | |
刊名 | IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
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2021 | |
卷号 | 8期号:2页码:971-983 |
关键词 | Adversarial attack and defense artificial intelligence security attack detection canonical variate analysis cyber security false data injection attack (FDIA) smart grid state estimation |
ISSN号 | 2327-4697 |
产权排序 | 1 |
英文摘要 | With the knowledge of the measurement configuration and the topology structure of a power system, attackers can launch false data injection attacks (FDIAs) without detection by existing bad data detection methods in state estimation. The attacks can also introduce errors to estimated state variables, which are critical to grid reliability and operation stability. Existing protection methods cannot handle dynamic and variable network configurations. In this paper, to effectively defend against FDIAs, we propose a canonical variate analysis based detection method which monitors the variation of statistical detection indicators T2 and Q about projected canonical variables before and after attacks. Unlike most statistic models that only consider cross-correlation of discretemeasurements constrained by Kirchhoff’s Law at each independent sampling time, we also consider the auto-correlation of measurements caused by time series characteristics of varying loads. Experiment results on IEEE-14 bus system demonstrate the effectiveness and accuracy of our proposedmethod based on both synthetically generated data and real-world electricity data from the New York independent system operator. |
资助项目 | National Natural Science Foundation of China[61673371] ; International Partnership Program of the Chinese Academy of Sciences[173321KYSB20180020] ; Liaoning Provincial Natural Science Foundation of China[2019-YQ-09] ; China Scholarship Council |
WOS关键词 | CYBER-SECURITY ; SMART ; NETWORK |
WOS研究方向 | Engineering ; Mathematics |
语种 | 英语 |
WOS记录号 | WOS:000680892400015 |
资助机构 | National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61673371] ; International Partnership Program of the Chinese Academy of Sciences [173321KYSB20180020] ; Liaoning Provincial Natural Science Foundation of China [2019-YQ-09] ; China Scholarship CouncilChina Scholarship Council |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/29356] ![]() |
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 | Xiao Y(肖杨); Liang W(梁炜) |
作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 2.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.Department of Computer Science, The University of Alabama, Tuscaloosa, AL 35487-0290 USA 5.University of Chinese Academy of Sciences, Beijing 100049, China |
推荐引用方式 GB/T 7714 | Pei C,Xiao Y,Liang W,et al. Detecting False Data Injection Attacks Using Canonical Variate Analysis in Power Grid[J]. IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING,2021,8(2):971-983. |
APA | Pei C,Xiao Y,Liang W,&Han XJ.(2021).Detecting False Data Injection Attacks Using Canonical Variate Analysis in Power Grid.IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING,8(2),971-983. |
MLA | Pei C,et al."Detecting False Data Injection Attacks Using Canonical Variate Analysis in Power Grid".IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 8.2(2021):971-983. |
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