Hybrid traffic classification approach based on decision tree

Authors:
Wei Lu;Mahbod Tavallaee;Ali A. Ghorbani
Affiliations:
Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada;Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada;Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada
Venue:
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Year:
2009

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Abstract

Classifying network traffic is very challenging and is still an issue yet to be solved due to the increase of new applications and traffic encryption. In this paper, we propose a novel hybrid approach for the network flow classification, in which we first apply the payload signature based classifier to identify the flow applications and unknown flows are then identified by a decision tree based classifier in parallel. We evaluate our approach with over 100 million flows collected over three consecutive days on a large-scale WiFi ISP network and results show the proposed approach successfully classifies all the flows with an accuracy approaching 93%.