Network traffic classification using correlation information

Zhang, Jun, Xiang, Yang, Wang, Yu, Zhou, Wanlei, Xiang, Yong and Guan, Yong 2013, Network traffic classification using correlation information, IEEE transactions on parallel and distributed systems, vol. 24, no. 1, pp. 104-117, doi: 10.1109/TPDS.2012.98.

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Title Network traffic classification using correlation information
Author(s) Zhang, JunORCID iD for Zhang, Jun
Xiang, YangORCID iD for Xiang, Yang
Wang, Yu
Zhou, WanleiORCID iD for Zhou, Wanlei
Xiang, YongORCID iD for Xiang, Yong
Guan, Yong
Journal name IEEE transactions on parallel and distributed systems
Volume number 24
Issue number 1
Start page 104
End page 117
Total pages 14
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2013
ISSN 1045-9219
Keyword(s) network operations
Traffic classification
Summary 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.
Language eng
DOI 10.1109/TPDS.2012.98
Field of Research 080503 Networking and Communications
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890199 Communication Networks and Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, IEEE
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