Optimizing Traffic Classification Using Hybrid Feature Selection

摘要：

The identification of network applications is of fundamental important to numerous network activities. Unfortunately, traditional port-based classification and packet payload-based analysis exhibit a number of shortfalls. A promising alternative is to use Machine Learning (ML) techniques and identify network applications based on per-flow features. Since a lot of flow features can be used for flow classification, the flow classifier may deal with huge amount of data, which contains irrelevant and redundant features causing slower training and testing process, higher resource consumption as well as poor classification accuracy. Therefore, feature selection plays a vital role in performance optimizing. In this paper, we propose a hybrid feature selection method for flow classification using Chi-Squared and C4.5 algorithm (ChiSquared-C4.5). The experiments demonstrate our approach can greatly improve computational performance without negative impact on classification accuracy.

关键词： Chi-Squared C4.5 Feature Selection

作者: Dai Lei Yun Xiaochun Xiao Jun

作者单位: Institute of Computing Technology Chinese Academy of Science

会议类型: 国际会议

会议名称: The Ninth International Conference on Web-Age Information Management(第九届web时代信息管理国际会议)(WAIM 2008)

会议地点: 张家界

会议语种:英文

在线出版日期: 2008-07-20（万方平台首次上网日期，不代表论文的发表时间）