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.
Cite
Text
Jr. et al. "In-the-Dark Network Traffic Classification Using Support Vector Machines." AAAI Conference on Artificial Intelligence, 2008.Markdown
[Jr. et al. "In-the-Dark Network Traffic Classification Using Support Vector Machines." AAAI Conference on Artificial Intelligence, 2008.](https://mlanthology.org/aaai/2008/jr2008aaai-dark/)BibTeX
@inproceedings{jr2008aaai-dark,
title = {{In-the-Dark Network Traffic Classification Using Support Vector Machines}},
author = {Jr., William H. Turkett and Karode, Andrew V. and Fulp, Errin W.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2008},
pages = {1745-1750},
url = {https://mlanthology.org/aaai/2008/jr2008aaai-dark/}
}