计算机工程与应用2019，Vol.55Issue(3)：68-75,8.DOI:10.3778/j.issn.1002-8331.1711-0100
基于概念漂移检测的自适应流量分类方法
Adaptive Traffic Classification Approach Based on Concept Drift Detection
摘要
Abstract
For network traffic characteristics will change with the change of network environment, which causes a signifi-cant reduction on the accuracy of traffic classification method based on machine learning. This paper proposes an adaptive traffic classification method based on concept drift detection, the method adopts Kolmogorov-Smirnov test to detect traffic concept drift, and then the classifier to be effectively updated through multi-view cooperative schema by introduction of new coming traffic in response to revise the model change caused by concept drift. Experimental results show that the method can effectively detect concept drift and update the classifier it has high accuracy and generalization.关键词
概念漂移/Kolmogorov-Smirnov检验/协同学习/流量分类Key words
concept drift/Kolmogorov-Smirnov test/cooperative learning/traffic classification分类
信息技术与安全科学引用本文复制引用
姜振东,王建明,潘吴斌..基于概念漂移检测的自适应流量分类方法[J].计算机工程与应用,2019,55(3):68-75,8.基金项目
国家自然科学基金(No.U1636201). （No.U1636201）
