Estimating flow distributions from sampled flow statistics
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Internet traffic classification using bayesian analysis techniques
SIGMETRICS '05 Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Semi-supervised network traffic classification
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Lightweight application classification for network management
Proceedings of the 2007 SIGCOMM workshop on Internet network management
Internet traffic classification demystified: myths, caveats, and the best practices
CoNEXT '08 Proceedings of the 2008 ACM CoNEXT Conference
Online EM for unsupervised models
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
On dominant characteristics of residential broadband internet traffic
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
A survey of techniques for internet traffic classification using machine learning
IEEE Communications Surveys & Tutorials
Timely and continuous machine-learning-based classification for interactive IP traffic
IEEE/ACM Transactions on Networking (TON)
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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.