A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids
False data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, data analytical methods have been employed to mitigate false data injection attacks (FDIAs), especially when large scale smart grids generate huge amounts of data. In this paper, a novel data...
| 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 |
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| Online Access: | https://ieeexplore.ieee.org/document/8093999/ |
| _version_ | 1823925057212121088 |
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| author | Yi Wang Mahmoud M. Amin Jian Fu Heba B. Moussa |
| author_facet | Yi Wang Mahmoud M. Amin Jian Fu Heba B. Moussa |
| author_sort | Yi Wang |
| collection | DOAJ |
| description | False data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, data analytical methods have been employed to mitigate false data injection attacks (FDIAs), especially when large scale smart grids generate huge amounts of data. In this paper, a novel data analytical method is proposed to detect FDIAs based on data-centric paradigm employing the margin setting algorithm (MSA). The performance of the proposed method is demonstrated through simulation using the six-bus power network in a wide area measurement system environment, as well as experimental data sets. Two FDIA scenarios, playback attack and time attack, are investigated. Experimental results are compared with the support vector machine (SVM) and artificial neural network (ANN). The results indicate that MSA yields better results in terms of detection accuracy than both the SVM and ANN when applied to FDIA detection. |
| first_indexed | 2024-12-16T20:02:53Z |
| format | Article |
| id | doaj.art-f422f73b2e4343efbb77c20e26fa7a39 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T20:02:53Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f422f73b2e4343efbb77c20e26fa7a392022-12-21T22:18:25ZengIEEEIEEE Access2169-35362017-01-015260222603310.1109/ACCESS.2017.27690998093999A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart GridsYi Wang0https://orcid.org/0000-0003-0171-2133Mahmoud M. Amin1Jian Fu2Heba B. Moussa3Department of Electrical and Computer Engineering, Manhattan College, Riverdale, NY, USADepartment of Electrical and Computer Engineering, Manhattan College, Riverdale, NY, USADepartment of Electrical Engineering and Computer Science, Alabama A&M University, Normal, AL, USADepartment of Electrical and Computer Engineering, City College of New York, New York, NY, USAFalse data injection cyber-physical threat is a typical integrity attack in modern smart grids. These days, data analytical methods have been employed to mitigate false data injection attacks (FDIAs), especially when large scale smart grids generate huge amounts of data. In this paper, a novel data analytical method is proposed to detect FDIAs based on data-centric paradigm employing the margin setting algorithm (MSA). The performance of the proposed method is demonstrated through simulation using the six-bus power network in a wide area measurement system environment, as well as experimental data sets. Two FDIA scenarios, playback attack and time attack, are investigated. Experimental results are compared with the support vector machine (SVM) and artificial neural network (ANN). The results indicate that MSA yields better results in terms of detection accuracy than both the SVM and ANN when applied to FDIA detection.https://ieeexplore.ieee.org/document/8093999/Data analyticalfalse data injectioncyber-physical attacksmart grid |
| spellingShingle | Yi Wang Mahmoud M. Amin Jian Fu Heba B. Moussa A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids IEEE Access Data analytical false data injection cyber-physical attack smart grid |
| title | A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids |
| title_full | A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids |
| title_fullStr | A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids |
| title_full_unstemmed | A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids |
| title_short | A Novel Data Analytical Approach for False Data Injection Cyber-Physical Attack Mitigation in Smart Grids |
| title_sort | novel data analytical approach for false data injection cyber physical attack mitigation in smart grids |
| topic | Data analytical false data injection cyber-physical attack smart grid |
| url | https://ieeexplore.ieee.org/document/8093999/ |
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