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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 7    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 7,July 2012
Traffic Classification Technique in Computer Networks
Full Text(PDF, )  PP.517-525  
S. M. Parvat, Prof. Dr. S. D. Lokhande
Machine Learning (ML), Internet Protocol (IP), Network Simulator version 2 (NS2).
Traffic classification enables a variety of applications and topics, including Quality of Service, security, monitoring, and intrusion-detection that are of use to researchers, accountants, network operators and end users. Capitalizing on network traffic that had been previously hand-classified provides with training and testing data-sets. The classification of network traffic can be done using Machine Learning Method, for this the use of simulating tools like NS2 can be used. It requires network protocol headers and the properties of unknown traffic for a successful classification stage.
[1] Denis Zuev and Andrew W. Moore “Traffic Classification using a Statistical Approach”

[2] Tom Auld, Andrew W. Moore, Member, IEEE, and Stephen F. Gull “Bayesian Neural Networks for Internet Traffic Classification”

[3] Andrew W. Moore and Denis Zuev “Internet Traffic Classification Using Bayesian Analysis Techniques”

[4] Andrew W. Moore, Denis Zuev, Michael Crogan “Discriminators for use in flow based classification”

[5] Thuy T.T. Nguyen and Grenville Armitage “A Survey of Techniques for Internet Traffic Classification using Machine Learning” IEEE Communications Surveys & Tutorials, Vol. 10, No. 4, 2008

[6] Byungchul Park and James Won-Ki Hong, Young J. Won, “Toward Fine-Grained Traffic Classification”

[7] Nigel Williams, Sebastian Zander, Grenville Armitage “A Preliminary Performance Comparison of Five Machine Learning Algorithms for Practical IP Traffic Flow Classification” ACM SIGCOMM Computer Communication Review 5 Volume 36, Number 5, October 2006

[8]http://www.cl.cam.ac.uk/research/srg/netos/nprobe/data/papers/sigmetric s/index.html


[10] Waikato Environment for Knowledge Analysis (WEKA) 3.4.4, http://www.cs.waikato.ac.nz/ml/weka/ (viewed August 2006).

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