网络安全与数据治理2025，Vol.44Issue(7)：15-19,26,6.DOI:10.19358/j.issn.2097-1788.2025.07.003
基于超参数优化和LightGBM算法的DDoS攻击检测与分类
DDoS attack detection and classification based on hyperparameter optimization and LightGBM algorithm
胡宏伟 1孙皓月1
作者信息
- 1. 河北建筑工程学院,河北 张家口 075000
- 折叠
摘要
Abstract
Aiming at the characteristics of large sample capacity and multiple data features of distributed denial of service attack(DDoS)data traffic as well as the problem of low detection and classification accuracy,this paper proposes a DDoS attack detec-tion and classification method based on LightGBM(Light Gradient Boosting Machine)algorithm.Based on the preprocessing and feature screening of the CICDDoS2019 dataset,the LightGBM detection model and multi-classification model are constructed.Meanwhile,random grid search and Bayesian hyperparameter optimisation techniques are used to achieve hyperparameters auto-tuning during model pre-training.The experimental results show that the model in this paper can achieve an accuracy rate of 98.34%in the detection and classification tasks.This research aims to provide an efficient and simple detection and classification idea for DDoS attacks.关键词
DDoS攻击/超参数优化/LightBGM/检测与分类Key words
DDoS attacks/hyperparameter optimization/LightBGM/detection and classification分类
信息技术与安全科学引用本文复制引用
胡宏伟,孙皓月..基于超参数优化和LightGBM算法的DDoS攻击检测与分类[J].网络安全与数据治理,2025,44(7):15-19,26,6.
