Machine learning-based classification of encrypted internet traffic

Authors:
Talieh Seyed Tabatabaei;Mostafa Adel;Fakhri Karray;Mohamed Kamel
Affiliations:
Centre for Pattern Analysis and Machine Intelligence (CPAMI), University of Waterloo, Waterloo, Ontario, Canada;Centre for Pattern Analysis and Machine Intelligence (CPAMI), University of Waterloo, Waterloo, Ontario, Canada;Centre for Pattern Analysis and Machine Intelligence (CPAMI), University of Waterloo, Waterloo, Ontario, Canada;Centre for Pattern Analysis and Machine Intelligence (CPAMI), University of Waterloo, Waterloo, Ontario, Canada
Venue:
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2012

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Abstract

Peer-to-peer (P2P) networking has introduced a major shift in the application and traffic mix of the Internet and established itself as the main driver of increasing traffic volume. The high requirements of some P2P applications result in network operational issues: these applications consume vast amounts of network resources and can prevent mission critical applications from accessing the network. Therefore the ability to correctly identify them can be crucial for many network management and measurement tasks. In this paper some flow-based statistical features of Internet traffic are investigated in order to detect P2P traffic. We propose a system to identify the BT traffic, which is one of the most popular and problematic P2P applications using support vector machines. The accuracy of 94.5% was achieved for recognizing encrypted traffic which is a very promising result.