Traffic Classification in Information Poor Environments
H. Rotsos, Jurgen van Gael and Andrew Moore
1st International Workshop on Traffic Analysis and Classification 2010.

Abstract

Traffic classification using machine learning continues to be an active research area. The majority of work in this area uses off-the-shelf machine learning tools and treats them as black-box classifiers. This approach turns all the modelling complexity into a feature selection problem. In this paper, we build a problem-specific solution to the traffic classifica- tion problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to de- sign classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and un- labelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features.

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