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

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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.

William H. Turkett Jr., Andrew V. Karode, Errin W.

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AAAI 2008 | In-the-dark Traffic Classification | Intelligent Agents | Support Vector Machines | SVM Feature Weights |

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Added	02 Oct 2010
Updated	02 Oct 2010
Type	Conference
Year	2008
Where	AAAI
Authors	William H. Turkett Jr., Andrew V. Karode, Errin W. Fulp

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

AAAI 2008 | In-the-dark Traffic Classification | Intelligent Agents | Support Vector Machines | SVM Feature Weights |

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