Traffic classification based on flow similarity

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. © 2009 Springer-Verlag.