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Network traffic classification using correlation information

journal contribution
posted on 2013-01-01, 00:00 authored by Jun Zhang, Yang Xiang, Yu Wang, Wanlei Zhou, Yong XiangYong Xiang, Y Guan
Traffic classification has wide applications in network management, from security monitoring to quality of service measurements. Recent research tends to apply machine learning techniques to flow statistical feature based classification methods. The nearest neighbor (NN)-based method has exhibited superior classification performance. It also has several important advantages, such as no requirements of training procedure, no risk of overfitting of parameters, and naturally being able to handle a huge number of classes. However, the performance of NN classifier can be severely affected if the size of training data is small. In this paper, we propose a novel nonparametric approach for traffic classification, which can improve the classification performance effectively by incorporating correlated information into the classification process. We analyze the new classification approach and its performance benefit from both theoretical and empirical perspectives. A large number of experiments are carried out on two real-world traffic data sets to validate the proposed approach. The results show the traffic classification performance can be improved significantly even under the extreme difficult circumstance of very few training samples.

History

Journal

IEEE transactions on parallel and distributed systems

Volume

24

Issue

1

Pagination

104 - 117

Publisher

IEEE

Location

Piscataway, N.J.

ISSN

1045-9219

eISSN

1558-2183

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2013, IEEE