Network Traffic Classification Using Correlation Information

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 s...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems 2013-01, Vol.24 (1), p.104-117
Main Authors: Zhang, Jun, Xiang, Yang, Wang, Yu, Zhou, Wanlei, Xiang, Yong, Guan, Yong
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Classification
Correlation
network operations
Networks
Neural networks
Performance enhancement
Robustness
security
Studies
Support vector machines
Traffic classification
Traffic engineering
Traffic flow
Training
Training data
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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.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2012.98