A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

查看全文 作　　者：Jieren [1,2]Cheng;Junqi [2]Li;Xiangyan [2]Tang;Victor [3]SSheng;Chen [2]Zhang;Mengyang [2]Li

机构地区：[1]Key Laboratory of Internet Information Retrieval of Hainan Province,Hainan University,Haikou,China;[2]College of Information Science&Technology,Hainan University,Haikou,China;[3]Department of Computer Science,University of Central Arkansas,Conway,AR 72035,USA

出　　处：《Computers, Materials & Continua》索引2020年第3期,共21页

基　　金：This work was supported by the Hainan Provincial Natural Science Foundation of China[2018CXTD333,617048];National Natural Science Foundation of China[61762033,61702539];Hainan University Doctor Start Fund Project[kyqd1328];Hainan University Youth Fund Project[qnjj1444]. 摘　　要：Distributed Denial of Service(DDoS)attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security.Existing detection methods cannot effectively detect early attacks.In this paper,we propose a detection method of DDoS attacks based on generalized multiple kernel learning(GMKL)combining with the constructed parameter R.The super-fusion feature value(SFV)and comprehensive degree of feature(CDF)are defined to describe the characteristic of attack flow and normal flow.A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm.A DDoS attack detection classifier is generated by using the trained GMKL model with R parameter.The experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection,and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate. 关键词：DDoS attack detection GMKL parameter optimization

相关文献

参考文献（33）
引证文献（1）
耦合文献（19）

参考文献(33)

引证文献(1)

耦合文献(19)

互联网出版许可证新出网证(渝)字10号全国400电话 - 免长途话费

A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning

参考文献(33)

引证文献(1)

耦合文献(19)

用户提示