Traffic Classification in Information Poor Environments
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.