A Method for P2P Traffic Identification Based on Semi-Supervised Learning

Lian Fa Wu

doi:10.4028/www.scientific.net/AMM.602-605.1933

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 602-605A Method for P2P Traffic Identification Based on...

A Method for P2P Traffic Identification Based on Semi-Supervised Learning

Abstract:

In recent years, Internet traffic classification using machine learning is a hot topic, and supervised learning methods which contain Support Vector Machine were used to identify Internet traffic in many papers. The supervised learning methods need many instances which have been labeled to train classifying model, but it is difficult to label the instances because many traffic have been encrypted. Labeled instances and unlabeled instances can be used by semi-supervised learning methods to train the classifying model, so that it is very fit for p2p traffic identification. Transductive support vector machine is one of the typical semi-supervised learning methods. Based on theoretic analyzing and experiment, we compared the accuracy of TSVM and SVM. The experiment results show that the semi-supervised methods have some advantages on identification of p2p traffic.

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Periodical:

Applied Mechanics and Materials (Volumes 602-605)

Pages:

1933-1937

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.602-605.1933

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Online since:

August 2014

Authors:

Lian Fa Wu*

Keywords:

P2P Traffic, Semi-Supervised Learning, Statistical Feature, Support Vector Machine (SVM)

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* - Corresponding Author

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