Abstract
As network traffic is dramatically increasing, classification of application traffic becomes important for the effective use of network resources. Classification of network traffic using port-based or payload-based analysis is becoming increasingly difficult because of many peer-to-peer (P2P) applications using dynamic port numbers, masquerading techniques, and encryption. An alternative approach is to classify traffic by exploiting the distinctive characteristics of applications. In this paper, we propose a classification method of application traffic using statistic signatures based on SVM (Support Vector Machine). The statistic signatures, defined as a directional sequence of packet size in a flow, are collected for each application, and applications are classified by SVM mechanism.
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Hwang, S., Cho, K., Kim, J., Baek, Y., Yun, J., Han, K. (2013). Traffic Classification Approach Based on Support Vector Machine and Statistic Signature. In: Balandin, S., Andreev, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networking. ruSMART NEW2AN 2013 2013. Lecture Notes in Computer Science, vol 8121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40316-3_29
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DOI: https://doi.org/10.1007/978-3-642-40316-3_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40315-6
Online ISBN: 978-3-642-40316-3
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