Traffic Classification Based on Flow Similarity

Authors:
Jae Yoon Chung;Byungchul Park;Young J. Won;John Strassner;James W. Hong
Affiliations:
Dept. of Computer Science and Engineering, POSTECH, Korea;Dept. of Computer Science and Engineering, POSTECH, Korea;Dept. of Computer Science and Engineering, POSTECH, Korea;Dept. of Computer Science and Engineering, POSTECH, Korea and Waterford Institute of Technology, Waterford, Ireland;Dept. of Computer Science and Engineering, POSTECH, Korea
Venue:
IPOM '09 Proceedings of the 9th IEEE International Workshop on IP Operations and Management
Year:
2009

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Abstract

Due to the various masquerading strategies adopted by newer P2P applications to avoid detection and filtering, well-known port mapping techniques cannot guarantee their accuracy any more. Alternative approaches, application-signature mapping, behavior-based analysis, and machine learning based classification methods, show more promising accuracy. However, these methods still have complexity issues. This paper provides a new classification method which utilizes cosine similarity between network flows.