Probabilistic graphical models for semi-supervised traffic classification

Rotsos, Charalampos and Gael, Jurgen Van and Moore, Andrew W. and Ghahramani, Zoubin (2010) Probabilistic graphical models for semi-supervised traffic classification. In: Proceedings of the 6th International Wireless Communications and Mobile Computing Conference on ZZZ - IWCMC '10. ACM, pp. 752-757. ISBN 9781450300629

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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 classification problem by designing a custom probabilistic graphical model. Graphical models are a modular framework to design classifiers which incorporate domain-specific knowledge. More specifically, our solution introduces semi-supervised learning which means we learn from both labelled and unlabelled traffic flows. We show that our solution performs competitively compared to previous approaches while using less data and simpler features.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
131964
Deposited By:
Deposited On:
18 Mar 2019 10:40
Refereed?:
Yes
Published?:
Published
Last Modified:
23 Nov 2020 02:31