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A Machine Learning Approach for Efficient Traffic Classification

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If you have a question about this talk, please contact Eiko Yoneki .

Online traffic classification continues to be of longterm interest to the networking community. It serves as the input for application modeling and practical solutions such as network monitoring, quality-of-service and intrusion-detection. In this paper we present a machine-learning approach that accurately classifies internet traffic using C4.5 decision tree. Accuracy is not our only concern; the latency and throughput are also of extreme importance. Without inspecting packet payload, our method can identify traffic of different types of applications with 99.8% total accuracy, by collecting 12 features at the start of the flows.

This talk is part of the Computer Laboratory Systems Research Group Seminar series.

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