Detection to false data for smart grid
Abstract False data injection attack in smart grid might does not launch interference and attack behaviors, so that this attack is difficult found. To address this, this paper proposed a conformal neural network detection method being sensitive to false data. Firstly, using the conformity scores cal...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-02-01
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| Series: | Cybersecurity |
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| Online Access: | https://doi.org/10.1186/s42400-024-00326-5 |
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| _version_ | 1823861977476235264 |
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| author | Jian Zheng Shumiao Ren Jingyue Zhang Yu Kui Jingyi Li Qin Jiang Shiyan Wang |
| author_facet | Jian Zheng Shumiao Ren Jingyue Zhang Yu Kui Jingyi Li Qin Jiang Shiyan Wang |
| author_sort | Jian Zheng |
| collection | DOAJ |
| description | Abstract False data injection attack in smart grid might does not launch interference and attack behaviors, so that this attack is difficult found. To address this, this paper proposed a conformal neural network detection method being sensitive to false data. Firstly, using the conformity scores calculated by the mathematical probability to identify the false data. Then, the neural network learns a boundary separating false data and normal data on the conformal region yielded by the conformity scores. Finally, experiments on simulated and real datasets indicate that the proposed method obtains 0.9738 detected accuracy, and the sensitivity to false data reaches 0.9387, defeating against the competitors, moreover, the proposed method does not exhibit exponential detection time as data volume augments. We demonstrate that evaluating the consistency between the data does not rely on data distributions and the operation status in this real-time system like smart grids, since conformity scores can calculate the mathematical probability following the same data distribution. The boundaries learned from conformal regions are independent of data distribution and the information of operation status in smart grids. This evaluation manner of data consistency and that of boundary learning are equally applicable to the identification and separation of those false data injected into other real-time systems. |
| format | Article |
| id | doaj-art-86fa10f89a2e4750be5255993ddb21f2 |
| institution | Kabale University |
| issn | 2523-3246 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Cybersecurity |
| spelling | doaj-art-86fa10f89a2e4750be5255993ddb21f22025-02-09T12:43:03ZengSpringerOpenCybersecurity2523-32462025-02-018112210.1186/s42400-024-00326-5Detection to false data for smart gridJian Zheng0Shumiao Ren1Jingyue Zhang2Yu Kui3Jingyi Li4Qin Jiang5Shiyan Wang6School of Artificial Intelligence, Chongqing Technology and Business UniversityCollege of Big Data, Chongqing Polytechnic InstituteCollege of Big Data, Chongqing Polytechnic InstituteCollege of Information Science and Technology and the Hebei Key Laboratory of Agricultural Big Data, Hebei Agricultural UniversityChongqing Key Laboratory of Public Big Data Security TechnologySchool of Smart Heath, Chongqing Polytechnic University of Electronic TechnologySchool of Communication and Information Engineering, Chongqing University of Posts and TelecommunicationsAbstract False data injection attack in smart grid might does not launch interference and attack behaviors, so that this attack is difficult found. To address this, this paper proposed a conformal neural network detection method being sensitive to false data. Firstly, using the conformity scores calculated by the mathematical probability to identify the false data. Then, the neural network learns a boundary separating false data and normal data on the conformal region yielded by the conformity scores. Finally, experiments on simulated and real datasets indicate that the proposed method obtains 0.9738 detected accuracy, and the sensitivity to false data reaches 0.9387, defeating against the competitors, moreover, the proposed method does not exhibit exponential detection time as data volume augments. We demonstrate that evaluating the consistency between the data does not rely on data distributions and the operation status in this real-time system like smart grids, since conformity scores can calculate the mathematical probability following the same data distribution. The boundaries learned from conformal regions are independent of data distribution and the information of operation status in smart grids. This evaluation manner of data consistency and that of boundary learning are equally applicable to the identification and separation of those false data injected into other real-time systems.https://doi.org/10.1186/s42400-024-00326-5False dataSmart gridData consistencyNeural networks |
| spellingShingle | Jian Zheng Shumiao Ren Jingyue Zhang Yu Kui Jingyi Li Qin Jiang Shiyan Wang Detection to false data for smart grid Cybersecurity False data Smart grid Data consistency Neural networks |
| title | Detection to false data for smart grid |
| title_full | Detection to false data for smart grid |
| title_fullStr | Detection to false data for smart grid |
| title_full_unstemmed | Detection to false data for smart grid |
| title_short | Detection to false data for smart grid |
| title_sort | detection to false data for smart grid |
| topic | False data Smart grid Data consistency Neural networks |
| url | https://doi.org/10.1186/s42400-024-00326-5 |
| work_keys_str_mv | AT jianzheng detectiontofalsedataforsmartgrid AT shumiaoren detectiontofalsedataforsmartgrid AT jingyuezhang detectiontofalsedataforsmartgrid AT yukui detectiontofalsedataforsmartgrid AT jingyili detectiontofalsedataforsmartgrid AT qinjiang detectiontofalsedataforsmartgrid AT shiyanwang detectiontofalsedataforsmartgrid |