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Design of Dynamic Network Security Defense Mechanism Driven by Light GBM
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Author(s): Yiyu Dai (Information Technology Development and Management Office, Huaqiao University, China), Junzheng Lu (Information Technology Development and Management Office, Huaqiao University, China), Zesen Li (Huaqiao University, China), Jiawei Li (Information Technology Development and Management Office, Huaqiao University, China)and Yunxi Lu (Graduate School of Management, St. Petersburg State University, Russia)
Copyright: 2026
Volume: 19
Issue: 1
Pages: 18
Source title:
International Journal of Information Technologies and Systems Approach (IJITSA)
Editor(s)-in-Chief: Sangbing (Jason) Tsai (International Engineering and Technology Institute (IETI), Hong Kong)and Wei Liu (Qingdao University, China)
DOI: 10.4018/IJITSA.397341
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Abstract
In today's complex and ever-changing network security environment, traditional static detection methods struggle to cope with the rapid evolution of attack methods and the real-time processing requirements of large-scale data traffic. To address these challenges, this study proposes a dynamic network security defense mechanism based on the light gradient boosting machine. It combines model parameter optimization, online updating strategies, and inference-acceleration methods to construct an intrusion detection system that achieves both high accuracy and low latency. To verify performance, two public datasets, NSL-KDD and CIC-IDS2017, are employed. The results show that the proposed model achieves an accuracy of 96.2% and an F1 score of 93.9% on NSL-KDD, and an accuracy of 95.8% and an F1 score of 93.9% on CIC-IDS2017. Its overall performance outperforms mainstream models such as support vector machine, random forest, and deep neural network approaches.
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