摘要
针对软件定义网络(SDN)易受分布式拒绝服务(DDoS)攻击这一缺陷,提出基于熵和支持向量机(SVM)算法的DDoS攻击检测方法。在对网络中的流信息进行熵值检测时,若熵值无法判断,则从Packet-In事件中解析出所需特征值,并运用SVM算法分类预测DDoS攻击状态。应用Mininet模拟器和RYU控制器建立模型进行仿真检测,检测精度较高,抵御DDoS攻击的实时性良好。
In response to the vulnerability of Software Defined Network(SDN)to Distributed Denial of Service(DDoS)attacks,a DDoS attack detection method based on entropy and Support Vector Machine(SVM)algorithm is proposed.When detecting the entropy value of flow information in the network,if the entropy value cannot be determined,the required feature values are parsed from the Packet-In event,and then SVM algorithm is used to classify and predict the DDoS attack status.Mininet simulator and RYU controller are applied to establish a model for simulation detection.The result shows that the detection accuracy is high and the real-time resistance to DDoS attacks is good.
作者
毛宇
高刃
MAO Yu;GAO Ren(Electrical and Information Engineering,Hubei University of Automotive Technology,Shiyan Hubei 442002,China)
出处
《重庆科技学院学报（自然科学版）》
CAS
2024年第2期50-55,共6页
Journal of Chongqing University of Science and Technology：Natural Sciences Edition
基金
2021年湖北省教育厅科学技术研究计划重点项目“智能工厂的5G∕WiFi网络多域共存关键技术研究”(D20211802)。
