Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid
Internet of Things (IoT) technologies have been broadly applied in smart grid for monitoring physical or environmental conditions. Especially, state estimation is an important IoT-based application in smart grid, which is used in system monitoring to get the best estimate of the power grid state thr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/7983355/ |
| _version_ | 1819162284915687424 |
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| author | Ruzhi Xu Rui Wang Zhitao Guan Longfei Wu Jun Wu Xiaojiang Du |
| author_facet | Ruzhi Xu Rui Wang Zhitao Guan Longfei Wu Jun Wu Xiaojiang Du |
| author_sort | Ruzhi Xu |
| collection | DOAJ |
| description | Internet of Things (IoT) technologies have been broadly applied in smart grid for monitoring physical or environmental conditions. Especially, state estimation is an important IoT-based application in smart grid, which is used in system monitoring to get the best estimate of the power grid state through an analysis of the meter measurements and power system topologies. However, false data injection attack (FDIA) is a severe threat to state estimation, which is known for the difficulty of detection. In this paper, we propose an efficient detection scheme against FDIA. First, two parameters that reflect the physical property of smart grid are investigated. One parameter is the control signal from the controller to the static Var compensator (CSSVC). A large CSSVC indicates there exists the intense voltage fluctuation. The other parameter is the quantitative node voltage stability index (NVSI). A larger NVSI indicates a higher vulnerability level. Second, according to the values of the CSSVC and NVSI, an optimized clustering algorithm is proposed to distribute the potential vulnerable nodes into several classes. Finally, based on these classes, a detection method is proposed for the real-time detection of the FDIA. The simulation results show that the proposed scheme can detect the FDIA effectively. |
| first_indexed | 2024-12-22T17:25:48Z |
| format | Article |
| id | doaj.art-fa520f9f4d4d4ebaacc31a420df60e58 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T17:25:48Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-fa520f9f4d4d4ebaacc31a420df60e582022-12-21T18:18:44ZengIEEEIEEE Access2169-35362017-01-015137871379810.1109/ACCESS.2017.27286817983355Achieving Efficient Detection Against False Data Injection Attacks in Smart GridRuzhi Xu0Rui Wang1Zhitao Guan2https://orcid.org/0000-0003-0901-8621Longfei Wu3Jun Wu4https://orcid.org/0000-0003-2483-6980Xiaojiang Du5School of Control and Computer Engineering, North China Electric Power University, Beijing, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing, ChinaSchool of Control and Computer Engineering, North China Electric Power University, Beijing, ChinaDepartment of Computer and Information Sciences, Temple University, Philadelphia, PA, USACollege of Information Security Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Computer and Information Sciences, Temple University, Philadelphia, PA, USAInternet of Things (IoT) technologies have been broadly applied in smart grid for monitoring physical or environmental conditions. Especially, state estimation is an important IoT-based application in smart grid, which is used in system monitoring to get the best estimate of the power grid state through an analysis of the meter measurements and power system topologies. However, false data injection attack (FDIA) is a severe threat to state estimation, which is known for the difficulty of detection. In this paper, we propose an efficient detection scheme against FDIA. First, two parameters that reflect the physical property of smart grid are investigated. One parameter is the control signal from the controller to the static Var compensator (CSSVC). A large CSSVC indicates there exists the intense voltage fluctuation. The other parameter is the quantitative node voltage stability index (NVSI). A larger NVSI indicates a higher vulnerability level. Second, according to the values of the CSSVC and NVSI, an optimized clustering algorithm is proposed to distribute the potential vulnerable nodes into several classes. Finally, based on these classes, a detection method is proposed for the real-time detection of the FDIA. The simulation results show that the proposed scheme can detect the FDIA effectively.https://ieeexplore.ieee.org/document/7983355/Smart gridstate estimationfalse data injection attackcontrol signalnode voltage stability index |
| spellingShingle | Ruzhi Xu Rui Wang Zhitao Guan Longfei Wu Jun Wu Xiaojiang Du Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid IEEE Access Smart grid state estimation false data injection attack control signal node voltage stability index |
| title | Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid |
| title_full | Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid |
| title_fullStr | Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid |
| title_full_unstemmed | Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid |
| title_short | Achieving Efficient Detection Against False Data Injection Attacks in Smart Grid |
| title_sort | achieving efficient detection against false data injection attacks in smart grid |
| topic | Smart grid state estimation false data injection attack control signal node voltage stability index |
| url | https://ieeexplore.ieee.org/document/7983355/ |
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