Internet Traffic Classification Using DBSCAN

摘要：

In recent years, a technique based on machine learning for Internet traffic classification has attracted more and more attentions. It not only overcomes some shortcomings of traditional classification technique based on port number, but also does not inspect the packet payload, which involves the security and privacy. In this paper, we apply an unsupervised machine learning approach based on DBSCAN algorithm. DBSCAN algorithm has three merits: (1) minimal requirements of domain knowledge to determine the input parameters;(2) discovery of clusters with arbitrary shapes;(3) good efficiency on large data set. Experiment results show that DBSCAN has better effectiveness and efficiency.

关键词： Traffic Classification Machine Learning DBSCAN

作者: Caihong Yang Fei Wang Benxiong Huang

作者单位: Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan, P.R.China

会议类型: 国际会议

会议名称: 2009 WASE International Conference on Information Engineering(2009年国际信息工程会议)(ICIE 2009)

会议地点: 太原

会议语种:英文

页码: 822-825

在线出版日期: 2009-07-10（万方平台首次上网日期，不代表论文的发表时间）