University of Cambridge > Talks.cam > Computer Laboratory Systems Research Group Seminar > A Machine Learning Approach for Efficient Traffic Classification

A Machine Learning Approach for Efficient Traffic Classification

Add to your list(s) Download to your calendar using vCal

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.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2017 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity