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.
This work was partly supported by the IT R&D program of MKE/IITA [2008-F-016-02, CASFI] and WCU program through the KSEF of MEST, Korea [R31-2008-000-10100-0].
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Chung, J.Y., Park, B., Won, Y.J., Strassner, J., Hong, J.W. (2009). Traffic Classification Based on Flow Similarity. In: Nunzi, G., Scoglio, C., Li, X. (eds) IP Operations and Management. IPOM 2009. Lecture Notes in Computer Science, vol 5843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04968-2_6
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DOI: https://doi.org/10.1007/978-3-642-04968-2_6
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