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Reseach Article

State Of Art Survey of Network Traffic Classification

Published on March 2012 by Sheetal S. Shinde, Sandeep P. Abhang
International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 7
March 2012
Authors: Sheetal S. Shinde, Sandeep P. Abhang
ee07ed4b-5a3a-472f-b5ba-c2ac3b5c3839

Sheetal S. Shinde, Sandeep P. Abhang . State Of Art Survey of Network Traffic Classification. International Conference in Computational Intelligence. ICCIA, 7 (March 2012), 36-40.

@article{
author = { Sheetal S. Shinde, Sandeep P. Abhang },
title = { State Of Art Survey of Network Traffic Classification },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 7 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 36-40 },
numpages = 5,
url = { /proceedings/iccia/number7/5144-1056/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A Sheetal S. Shinde
%A Sandeep P. Abhang
%T State Of Art Survey of Network Traffic Classification
%J International Conference in Computational Intelligence
%@ 0975-8887
%V ICCIA
%N 7
%P 36-40
%D 2012
%I International Journal of Computer Applications
Abstract

This is a review paper of Network Traffic Classification Techniques. The survey looks at network traffic classification methods used by researchers as well as emerging research into the application of Machine Learning (ML) techniques to IP traffic classification. Current popular methods such as port numbers and payload–based identification exhibit a number of shortfalls, an alternative is to use ML techniques. It is also required to detect network applications based on flow statistics. The paper also take a review of clustering algorithms as well as various important approaches to semi-supervised learning The survey concludes that the K-means is a fastest algorithm as compared to DBSCAN and AutoClass. Another conclusion is the semi-supervised approach is a best classification approach for network traffic classification as compared with other techniques.

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Index Terms

Computer Science
Information Sciences

Keywords

TCP Traffic Classification Machine Learning (ML) unsupervised clustering supervised learning semi-supervised learning