In-the-Dark Network Traffic Classification Using Support Vector Machines

Abstract

This work addresses the problem of in-the-dark traffic classification for TCP sessions, an important problem in network management. An innovative use of support vector machines (SVMs) with a spectrum representation of packet flows is demonstrated to provide a highly accurate, fast, and robust method for classifying common application protocols. The use of a linear kernel allows for an analysis of SVM feature weights to gain insight into the underlying protocol mechanisms.

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Cite this paper

@inproceedings{Turkett2008IntheDarkNT, title={In-the-Dark Network Traffic Classification Using Support Vector Machines}, author={William H. Turkett and Andrew V. Karode and Errin W. Fulp}, booktitle={AAAI}, year={2008} }