文献类型：期刊文献

英文题名：Scalable kernel convex hull online support vector machine for intelligent network traffic classification

作者：Gu, Xiaoqing[1];Ni, Tongguang[1];Fan, Yiqing[2];Wang, Weibo[3]

机构：[1]Changzhou Univ, Sch Informat Sci & Engn, Changzhou 213164, Peoples R China;[2]Univ Southern Calif, Viterbi Sch Engn, Los Angeles, CA 90089 USA;[3]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：75

期号：7-8

起止页码：471

外文期刊名：ANNALS OF TELECOMMUNICATIONS

收录：;EI(收录号：20202608862494);WOS:【SCI-EXPANDED(收录号:WOS:000541211000001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grants 61976028 and 61806026 and by the Natural Science Foundation of Jiangsu Province under Grant BK 20180956.

语种：英文

外文关键词：Online learning; Support vector machine; Scalable kernel convex hull; Network traffic classification

摘要：Online support vector machine (SVM) is an effective learning method in real-time network traffic classification tasks. However, due to its geometric characteristics, the traditional online SVMs are sensitive to noise and class imbalance. In this paper, a scalable kernel convex hull online SVM called SKCHO-SVM is proposed to solve this problem. SKCHO-SVM involves two stages: (1) offline leaning stage, in which the noise points are deleted and initial pin-SVM classifier is built; (2) online updating stage, in which the classifier is updated with newly arrived data points, while carrying out the classification task. The noise deleting strategy and pinball loss function ensure SKCHO-SVM insensitive to noise data flows. Based on the scalable kernel convex hull, a small amount of convex hull vertices are dynamically selected as the training data points in each class, and the obtained scalable kernel convex hull can relieve class imbalance. Theoretical analysis and numerical experiments show that SKCHO-SVM has the distinctive ability of training time and classification performance.