The ADBPSO-LightGBM internal threat detection framework based on hybrid data balancing
The misuse of privileges by users can lead to significant reputational and financial losses for enterprises. To reduce the risk of information leakage, it is crucial to detect and analyze abnormal behaviours of internal employees. Firstly, based on the characteristics of internal employee behaviour,...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Systems Science & Control Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2025.2498913 |
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| _version_ | 1849311778900541440 |
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| author | Jin-Jie Zheng Xiu Kan Jian-Zhen Wu Zhen Zhang Xiu-Yu Gao |
| author_facet | Jin-Jie Zheng Xiu Kan Jian-Zhen Wu Zhen Zhang Xiu-Yu Gao |
| author_sort | Jin-Jie Zheng |
| collection | DOAJ |
| description | The misuse of privileges by users can lead to significant reputational and financial losses for enterprises. To reduce the risk of information leakage, it is crucial to detect and analyze abnormal behaviours of internal employees. Firstly, based on the characteristics of internal employee behaviour, a data filter strategy based on user behaviour is proposed. Then, a data balancing strategy based on the concept of hybrid sampling is introduced. Moreover, to further construct the behaviour model, an improved particle swarm optimization algorithm based on adaptive delay and genetic factors is proposed, and it is used to search for the optimal parameters of LightGBM. Experimental results demonstrate that the proposed method is highly effective in detecting internal threats. |
| format | Article |
| id | doaj-art-982c5aa5ad8d4641b50509894dd21f01 |
| institution | Kabale University |
| issn | 2164-2583 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj-art-982c5aa5ad8d4641b50509894dd21f012025-08-20T03:53:17ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832025-12-0113110.1080/21642583.2025.2498913The ADBPSO-LightGBM internal threat detection framework based on hybrid data balancingJin-Jie Zheng0Xiu Kan1Jian-Zhen Wu2Zhen Zhang3Xiu-Yu Gao4School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, People’s Republic of ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, People’s Republic of ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, People’s Republic of ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, People’s Republic of ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, People’s Republic of ChinaThe misuse of privileges by users can lead to significant reputational and financial losses for enterprises. To reduce the risk of information leakage, it is crucial to detect and analyze abnormal behaviours of internal employees. Firstly, based on the characteristics of internal employee behaviour, a data filter strategy based on user behaviour is proposed. Then, a data balancing strategy based on the concept of hybrid sampling is introduced. Moreover, to further construct the behaviour model, an improved particle swarm optimization algorithm based on adaptive delay and genetic factors is proposed, and it is used to search for the optimal parameters of LightGBM. Experimental results demonstrate that the proposed method is highly effective in detecting internal threats.https://www.tandfonline.com/doi/10.1080/21642583.2025.2498913Internal threatfeature selectiondata balancinghigh-dimensional clusteringparticle swarm algorithmthreat detection |
| spellingShingle | Jin-Jie Zheng Xiu Kan Jian-Zhen Wu Zhen Zhang Xiu-Yu Gao The ADBPSO-LightGBM internal threat detection framework based on hybrid data balancing Systems Science & Control Engineering Internal threat feature selection data balancing high-dimensional clustering particle swarm algorithm threat detection |
| title | The ADBPSO-LightGBM internal threat detection framework based on hybrid data balancing |
| title_full | The ADBPSO-LightGBM internal threat detection framework based on hybrid data balancing |
| title_fullStr | The ADBPSO-LightGBM internal threat detection framework based on hybrid data balancing |
| title_full_unstemmed | The ADBPSO-LightGBM internal threat detection framework based on hybrid data balancing |
| title_short | The ADBPSO-LightGBM internal threat detection framework based on hybrid data balancing |
| title_sort | adbpso lightgbm internal threat detection framework based on hybrid data balancing |
| topic | Internal threat feature selection data balancing high-dimensional clustering particle swarm algorithm threat detection |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2025.2498913 |
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