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Reseach Article

Network Traffic Classification based on Unsupervised Approach

Published on May 2013 by Pallavi Singhal, Rajeev Mathur, Himani Vyas
International Conference on Recent Trends in Engineering and Technology 2013
Foundation of Computer Science USA
ICRTET - Number 1
May 2013
Authors: Pallavi Singhal, Rajeev Mathur, Himani Vyas
c246940d-2d49-4e77-a5e6-3dd5fd7620bb

Pallavi Singhal, Rajeev Mathur, Himani Vyas . Network Traffic Classification based on Unsupervised Approach. International Conference on Recent Trends in Engineering and Technology 2013. ICRTET, 1 (May 2013), 5-11.

@article{
author = { Pallavi Singhal, Rajeev Mathur, Himani Vyas },
title = { Network Traffic Classification based on Unsupervised Approach },
journal = { International Conference on Recent Trends in Engineering and Technology 2013 },
issue_date = { May 2013 },
volume = { ICRTET },
number = { 1 },
month = { May },
year = { 2013 },
issn = 0975-8887,
pages = { 5-11 },
numpages = 7,
url = { /proceedings/icrtet/number1/11759-1303/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Recent Trends in Engineering and Technology 2013
%A Pallavi Singhal
%A Rajeev Mathur
%A Himani Vyas
%T Network Traffic Classification based on Unsupervised Approach
%J International Conference on Recent Trends in Engineering and Technology 2013
%@ 0975-8887
%V ICRTET
%N 1
%P 5-11
%D 2013
%I International Journal of Computer Applications
Abstract

The IP network engineering, management and control are highly benefited by Network traffic classification and application identifi¬cation. There are many popular methods available namely port-based and payload-based but they have shown some disadvantages, and the machine learning based method is a potential one. Unsupervised learning deals with a class of problems in which one seeks to determine how the data are organized. The difference between it and supervised learning in that the learner is given only unlabeled examples. Unsupervised learning is a way to form 'natural groupings' or clusters of patterns. Unsupervised learning is useful to the problem of density estimation in statistics. One form of unsupervised learning is clustering.

References
  1. The Monitoring System Based on Traffic Classification Ali Asghar Yarifard and Mohammad Hossein Yaghmaee Islamic Azad University, Qaenat Branch, Iran Department of Computer Engineering, and Ferdowsi University.
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Index Terms

Computer Science
Information Sciences

Keywords

Classification Clustering Machine Learning Semi Supervised Unsupervised Supervised