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1. NETWORK TRAFFIC CLASSIFICATION USING CORRELATION INFORMATION
ABSTRACT:
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.
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