Online Automatic Traffic Classification Architecture in Access Network

摘要：

Recently traffic classifications based on Statistics methods and Machine Learning techniques have attracted a great deal of interest. Some challenging issues for these methods are that most of them need prior analysis to detect traffic applications and training data sets to generate classification model offline; some require a high amount computation and memory resource. These are infeasible to cope with the fast growing number of new applications and online traffic classifications. We propose an online automatic traffic classification architecture using unsupervised machine learning technique, in which flows are automatically clustered based on sub-flow statistical features instead of full flows. We select Best-first features algorithm to find an optimal feature-sets which is suited for access network, then map the traffic flows to applications based on maximized probabilities applications in the clusters. The experiment results demonstrate the efficiency and capability of the proposed automated classification architecture.

关键词： Traffic classification machine learning unsupervised clustering access network

作者: Jian Zhang Zongjue Qian Guochu Shou Yihong Hu

作者单位: Beijing University of Posts and Telecommunications China Qingdao University of Technology China Beijing University of Posts and Telecommunications China

会议类型: 国际会议

会议名称: 2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI2009)

会议地点: 北京

会议语种:英文

页码: 2152-2157

在线出版日期: 2009-08-16（万方平台首次上网日期，不代表论文的发表时间）