BLINC: multilevel traffic classification in the dark
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
A P2P Network Traffic Classification Method Using SVM
ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers
IEEE Transactions on Pattern Analysis and Machine Intelligence
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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In this paper we propose a P2P network traffic classification method using nu-Maximal Margin Spherical Structured Multiclass Support Vector Machine (nu-MSMSVM) classifier. The P2P network traffic is classified into different classes based on four applications namely, Bit Torrent, PPLive, Skype and MSN. The concept of Hypersphere based classifiers being able to minimize the effect of outliers has been adapted in this work. The experimental results show low false positive and false negative ratio thereby achieving high precision and recall rate.