Security situational awareness of power information networks based on machine learning algorithms
To properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. A perception model outlines the consequences of the abstracted perception problem. Sample data is initially pre-process...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/09540091.2023.2284649 |
| _version_ | 1797450014100815872 |
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| author | Chao Wang Jia-han Dong Guang-xin Guo Tian-yu Ren Xiao-hu Wang Ming-yu Pan |
| author_facet | Chao Wang Jia-han Dong Guang-xin Guo Tian-yu Ren Xiao-hu Wang Ming-yu Pan |
| author_sort | Chao Wang |
| collection | DOAJ |
| description | To properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. A perception model outlines the consequences of the abstracted perception problem. Sample data is initially pre-processed using linear discriminant analysis methods to optimise the data, get integrated features, and ascertain the best projection. To assess system safety posture and find mapping relationships with network posture values, the cleaned data is subsequently input into an RBF neural network as training data. The reliability of the suggested technique for network security posture analysis is finally shown by simulations using the KDD Cup99 dataset and attack data from power information networks, with detection rates frequently surpassing 90%. |
| first_indexed | 2024-03-09T14:33:26Z |
| format | Article |
| id | doaj.art-2622c82b7d2b4ad9a5f79023b21232fc |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-09T14:33:26Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-2622c82b7d2b4ad9a5f79023b21232fc2023-11-27T15:47:46ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.2284649Security situational awareness of power information networks based on machine learning algorithmsChao Wang0Jia-han Dong1Guang-xin Guo2Tian-yu Ren3Xiao-hu Wang4Ming-yu Pan5State Grid Beijing Electric Power Company Electric Power Research Institute, Beijing, People’s Republic of ChinaState Grid Beijing Electric Power Company Electric Power Research Institute, Beijing, People’s Republic of ChinaState Grid Beijing Electric Power Company Electric Power Research Institute, Beijing, People’s Republic of ChinaState Grid Beijing Electric Power Company Electric Power Research Institute, Beijing, People’s Republic of ChinaState Grid Beijing Electric Power Company Electric Power Research Institute, Beijing, People’s Republic of ChinaState Grid Beijing Electric Power Company Electric Power Research Institute, Beijing, People’s Republic of ChinaTo properly predict the security posture of these networks, we provide a method based on machine learning algorithms to detect the security condition of power information networks. A perception model outlines the consequences of the abstracted perception problem. Sample data is initially pre-processed using linear discriminant analysis methods to optimise the data, get integrated features, and ascertain the best projection. To assess system safety posture and find mapping relationships with network posture values, the cleaned data is subsequently input into an RBF neural network as training data. The reliability of the suggested technique for network security posture analysis is finally shown by simulations using the KDD Cup99 dataset and attack data from power information networks, with detection rates frequently surpassing 90%.https://www.tandfonline.com/doi/10.1080/09540091.2023.2284649Cyber security situational awarenesspower information networkcyber-attacklinear discriminant analysis (LDA)RBF |
| spellingShingle | Chao Wang Jia-han Dong Guang-xin Guo Tian-yu Ren Xiao-hu Wang Ming-yu Pan Security situational awareness of power information networks based on machine learning algorithms Connection Science Cyber security situational awareness power information network cyber-attack linear discriminant analysis (LDA) RBF |
| title | Security situational awareness of power information networks based on machine learning algorithms |
| title_full | Security situational awareness of power information networks based on machine learning algorithms |
| title_fullStr | Security situational awareness of power information networks based on machine learning algorithms |
| title_full_unstemmed | Security situational awareness of power information networks based on machine learning algorithms |
| title_short | Security situational awareness of power information networks based on machine learning algorithms |
| title_sort | security situational awareness of power information networks based on machine learning algorithms |
| topic | Cyber security situational awareness power information network cyber-attack linear discriminant analysis (LDA) RBF |
| url | https://www.tandfonline.com/doi/10.1080/09540091.2023.2284649 |
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