In-the-dark network traffic classification using support vector machines
William H. Turkett, Andrew V. Karode, Errin W. Fulp · 2008
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.