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Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid | |
Liu, Yifa1,2![]() ![]() | |
发表期刊 | IEEE Transactions on Control of Network Systems
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ISSN | 2325-5870 |
2022-09 | |
卷号 | 9期号:3页码:1238-1250 |
文章类型 | 期刊论文 |
摘要 | As one of the most dangerous cyber attacks in smart grids, the false data injection attacks pose a serious threat to power system security. To detect the false data, the traditional residual method and other improved methods, such as the Kalman-filter-based detector, have been proposed. However, these methods often have defects, especially in a very complex networked system with noises. By investigating the tolerance to the uncertainty in the residual detection method and properties of noises, the attack magnitude planning has been presented to hide the attack behind noises, which can bypass the residual detection method. As to the Kalman-filter-based detector, this article designs a specific attack strategy that can successfully deceive the Kalman-filter-based detector. Under this strategy, the false data injected at each step are used to balance the anomalies caused by previous false data, making the system look quite normal in monitoring, while deviating the system from normal operation eventually. |
关键词 | Attack sequence false data injection Kalman filter smart grid security state estimation |
DOI | 10.1109/TCNS.2022.3141026 |
关键词[WOS] | SYSTEMS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61633016] ; National Natural Science Foundation of China[61873268] ; National Natural Science Foundation of China[62025307] ; National Natural Science Foundation of China[U1913209] ; Beijing Natural Science Foundation[JQ19020] |
项目资助者 | National Natural Science Foundation of China ; Beijing Natural Science Foundation |
WOS研究方向 | Automation & Control Systems ; Computer Science |
WOS类目 | Automation & Control Systems ; Computer Science, Information Systems |
WOS记录号 | WOS:000856122100019 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 复杂系统理论与方法 |
国重实验室规划方向分类 | 复杂系统建模与推演 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50129 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Cheng, Long |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Liu, Yifa,Cheng, Long. Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid[J]. IEEE Transactions on Control of Network Systems,2022,9(3):1238-1250. |
APA | Liu, Yifa,&Cheng, Long.(2022).Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid.IEEE Transactions on Control of Network Systems,9(3),1238-1250. |
MLA | Liu, Yifa,et al."Relentless False Data Injection Attacks Against Kalman-Filter-Based Detection in Smart Grid".IEEE Transactions on Control of Network Systems 9.3(2022):1238-1250. |
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