The ADBPSO-LightGBM internal threat detection framework based on hybrid data balancing
Year: 2025
Type: article
Abstract: 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.
Authors: Jin-Jie Zheng, Xiu Kan, Jian-Zhen Wu, Zhen Zhang, Xiu-Yu Gao
Institution: Shanghai University of Engineering Science
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,165
OpenAlex ID: W4410200990