SDN环境下基于Rényi RF XGBoost的DDoS攻击检测研究  

Research on Detection of DDoS Attack based on Rényi RF XGBoost in SDN

作　　者：杨桂芹[1] 张蔚[1] 张若 YANG Guiqin;ZHANG Wei;ZHANG Ruo(School of Electronics and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学,电子与信息工程学院,兰州730070

出　　处：《兰州交通大学学报》2025年第1期28-38,共11页Journal of Lanzhou Jiaotong University

基　　金：国家自然科学基金“融合两域数据及深度卷积神经网络的结构损伤识别方法研究”(62361034)。

摘　　要：DDoS攻击会对SDN造成毁灭性的打击,如何高效精准地检测出DDoS攻击就显得尤为重要。针对该问题,提出了一种在SDN环境下基于Rényi RF XGBoost的DDoS攻击检测方案。使用Rényi熵提取特征并对随机森林进行改进,通过集成学习将其与XGBoost进行融合,对网络流量进行分类预测,从而实现针对DDoS攻击的检测。此外,采用交叉熵损失和袋外误差对所提模型进行评价,通过相关检测指标对实验结果进行实时观察验证。结果表明,所提出的方法不仅有较低的交叉熵损失和袋外误差,相比于其他方法还提高了检测精度、精确率和召回率,缩短了检测时间,降低了误报率。DDoS attack will cause devastating damage to SDN,so it is particularly important to detect DDoS attack efficiently and accurately.To solve this problem,a detection plan for DDoS attack based on Rényi RF XGBoost in SDN environment is proposed.Rényi entropy is used to extract features and improve the Random Forest,then integrated with XGBoost through ensemble learning to classify and predict network traffic,thus realizing the detection of DDoS attacks.In addition,cross entropy loss and out of bag error are used to evaluate the proposed model,and the experimental results are validated by real time observation through the relevant testing metrics.The results show that the proposed method not only has lower cross entropy loss and out of bag error,but also improves the detection precision,accuracy rate and recall rate,shorts the detection time and reduces the false alarm rate compared with other methods.

关 键 词：SDN DDOS Rényi RF XGBoost 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]