A NOVEL WEIGHTED COMBINATION TECHNIQUE FOR TRAFFIC CLASSIFICATION

摘要：

　　Accurate classification of traffic flows is highly beneficial for network management and security monitoring.Nowadays,many researchers have proposed machine learning techniques (i.e.,decision tree,SVM,BayesNet and Naive Bayes) for traffic classification.However,none of these classification techniques can achieve the highest accuracy for all traffic classification tasks.Recently,more and more researchers tried to combine multiple classifiers to obtain better performance.In this paper,we propose a weighted combination technique for traffic classification.The weighted combination approach first takes advantage of the confidence values inferred by each individual classifier; then assigns weight for each classifier according to its prediction accuracy on a validation traffic dataset.Experimental results on two different traffic traces demonstrate that our new weighted multi-classification framework is able to obtain satisfactory results.

关键词： Traffic classification Combination technique

作者: Jinghua Yan Xiaochun Yun Zhigang Wu Hao Luo Shuzhuang Zhang

作者单位: School of Computer Science,Beijing University of Posts and Telecommunications,Beijing 100876,China National Computer Network Emergency Response Technical Team / Coordination Center of China,Beijing 1 Institute of Network Technology,Beijing University of Posts and Telecommunications,Beijing 100876,Ch

会议类型: 国际会议

会议名称: 2012 2nd IEEE International Conference on Cloud Computing and Intelligence Systems (2012年第2届IEEE云计算与智能系统国际会议(IEEE CCIS2012))

会议地点: 杭州

会议语种:英文

页码: 986-990

在线出版日期: 2012-10-30（万方平台首次上网日期，不代表论文的发表时间）