Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models
False data injection (FDI) attacks commonly target smart grids. Using the tools that are now available for detecting incorrect data, it is not possible to identify FDI attacks. One way that can be used to identify FDI attacks is machine learning. The purpose of this study is to analyse each of the s...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.964305/full |
| _version_ | 1811220414418911232 |
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| author | Saddam Aziz Muhammad Irshad Sami Ahmed Haider Jianbin Wu Ding Nan Deng Sadiq Ahmad |
| author_facet | Saddam Aziz Muhammad Irshad Sami Ahmed Haider Jianbin Wu Ding Nan Deng Sadiq Ahmad |
| author_sort | Saddam Aziz |
| collection | DOAJ |
| description | False data injection (FDI) attacks commonly target smart grids. Using the tools that are now available for detecting incorrect data, it is not possible to identify FDI attacks. One way that can be used to identify FDI attacks is machine learning. The purpose of this study is to analyse each of the six supervised learning (SVM-FS) hybrid techniques using the six different boosting and feature selection (FS) methodologies. A dataset from the smart grid is utilised in the process of determining the applicability of various technologies. Comparisons of detection strategies are made based on how accurately each one can identify different kinds of threats. The performance of classification algorithms that are used to detect FDI assaults is improved by the application of supervised learning and hybrid methods in a simulated exercise. |
| first_indexed | 2024-04-12T07:41:15Z |
| format | Article |
| id | doaj.art-b34ba001c29747e98e0522a4e9ce48d9 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-12T07:41:15Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-b34ba001c29747e98e0522a4e9ce48d92022-12-22T03:41:48ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2022-08-011010.3389/fenrg.2022.964305964305Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning modelsSaddam Aziz0Muhammad Irshad1Sami Ahmed Haider2Jianbin Wu3Ding Nan Deng4Sadiq Ahmad5Centre for Advances in Reliability and Safety, New Territories, Hong Kong, ChinaDepartment of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Computing, University of Worcester, Henwick Grove, United KingdomDepartment of Computer Science and Engineering, Zhejiang Normal University, Jinhua, ChinaSchool of Physics and Electronic Engineering, Jiaying University, Meizhou, ChinaECE Department COMSATS University Islamabad, Wah Cantt, PakistanFalse data injection (FDI) attacks commonly target smart grids. Using the tools that are now available for detecting incorrect data, it is not possible to identify FDI attacks. One way that can be used to identify FDI attacks is machine learning. The purpose of this study is to analyse each of the six supervised learning (SVM-FS) hybrid techniques using the six different boosting and feature selection (FS) methodologies. A dataset from the smart grid is utilised in the process of determining the applicability of various technologies. Comparisons of detection strategies are made based on how accurately each one can identify different kinds of threats. The performance of classification algorithms that are used to detect FDI assaults is improved by the application of supervised learning and hybrid methods in a simulated exercise.https://www.frontiersin.org/articles/10.3389/fenrg.2022.964305/fullsmart gridcyber-attackfalse data injectionfeature selectionclassification algorithms |
| spellingShingle | Saddam Aziz Muhammad Irshad Sami Ahmed Haider Jianbin Wu Ding Nan Deng Sadiq Ahmad Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models Frontiers in Energy Research smart grid cyber-attack false data injection feature selection classification algorithms |
| title | Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models |
| title_full | Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models |
| title_fullStr | Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models |
| title_full_unstemmed | Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models |
| title_short | Protection of a smart grid with the detection of cyber- malware attacks using efficient and novel machine learning models |
| title_sort | protection of a smart grid with the detection of cyber malware attacks using efficient and novel machine learning models |
| topic | smart grid cyber-attack false data injection feature selection classification algorithms |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.964305/full |
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