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

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.

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: William H. Turkett Jr., Andrew V. Karode, Errin W. Fulp
• Keywords: AAAI 2008, In-the-dark Traffic Classification, Intelligent Agents, Support Vector Machines, SVM Feature Weights