徐州工程学院学报（自然科学版）2025，Vol.40Issue(4)：42-49,8.
基于天鹰优化算法的XGBoost模型的网络入侵检测研究
Tianying Algorithm-Optimized XGBoost for Network Intrusion Detection
摘要
Abstract
In response to the limitations of existing network intrusion detection algorithms,such as low accuracy and excessively long detection times,an XGBoost algorithm optimized using the Tianying algorithm has been designed.First,the data set entering the local network is preprocessed to improve its dimensionality.Then,the tree structure of XGBoost is established to split the objective function synchronously and evaluate the gap between the expected value and the output value.Finally,the Tianying optimization algorithm is introduced to dynamically select the key parameter values at different iteration stages.A weighted histogram is then used to accurately select the splitting points,controlling the scale of the tree structure and reducing the model's complexity.Experimental results demonstrate that the proposed optimization algorithm exhibits strong iterative efficiency.The accuracy rates of the training and test sets are 99.3％and 99.4％,respectively.It also has certain advantages in terms of controlling the detection time.Therefore,it can be concluded that the Tianying-optimized XGBoost algorithm is applicable to intrusion detection.关键词
XGBoost模型/天鹰算法/加权直方图/迭代效率Key words
XGBoost algorithm/Tianying optimization algorithm/enhanced histogram/iterative efficiency分类
信息技术与安全科学引用本文复制引用
CAI Jiyong..基于天鹰优化算法的XGBoost模型的网络入侵检测研究[J].徐州工程学院学报（自然科学版）,2025,40(4):42-49,8.基金项目
滁州城市职业学院校级科学研究项目(2024zkyb03 （）
2024skyb13) （）
