An Empirical Investigation of Filter Attribute Selection Techniques for High-Speed Network Traffic Flow Classification

Authors:
Jie Yang;Jing Ma;Gang Cheng;Yixuan Wang;Lun Yuan;Chao Dong
Affiliations:
Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China and , ...;Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China;Microsoft Corporation, Redmond, USA;Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China;Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China;Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
Venue:
Wireless Personal Communications: An International Journal
Year:
2012

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Abstract

Attribute selection is an important methodology for data mining problems. Removing irrelevant and redundant attributes from original data set can greatly simplify building classifier models. In this paper, we consider applying attribute selection techniques to network traffic flow classification and conduct experiments using the actual network data collected from the Internet of China. The results show that building with an appropriate attribute selection method can simplify the network traffic classifier while achieving satisfactory classification accuracy.