期刊名称：International Journal of Grid and Distributed Computing
印刷版ISSN：2005-4262
出版年度：2015
卷号：8
期号：3
页码：309-322
DOI：10.14257/ijgdc.2015.8.3.29
出版社：SERSC
摘要：With development of scale, diversity and complexity of network traffic, the drawbacks of traditional machine learning methods on traffic classification is gradually exposed, especially the false positive problem in large-scale real network traffic classification is particularly serious. In this paper, aiming at reducing the false positive rate of network traffic classification, an effective network traffic classification method --- CMM method. CMM method contains three steps, including dividing the training set into clusters, forming sub-classifiers, and classifier integration in accordance with the principle of minimization and maximization. In this paper, we firstly demonstrate the effectiveness of this method in reducing the false positive rate. Secondly, we conduct experiments in large-scale national backbone network, such as the SSL protocol classification and experimental results verify the effectiveness of this method in large-scale the actual network traffic classification.
