Internet Traffic Classification: A Machine Learning Approach - Couverture souple

Singh, Kuldeep; Agrawal, Sunil

 
9783846595619: Internet Traffic Classification: A Machine Learning Approach
Couverture souple
ISBN 10 : 3846595616 ISBN 13 : 9783846595619
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

With rapid growth of internet traffic over last few years, the area of internet traffic classification becomes very significant for various ISPs. Now days, traditional internet traffic classification techniques such as port number and payload based techniques are seldom used because of use of dynamic port number instead of well-known port number in packet headers and various cryptographic techniques used to encrypt packet payload. Current trends are use of machine learning techniques for internet traffic classification. In this research work, downloaded internet traffic dataset, self-developed internet traffic datasets for packet capture duration of 2 minute and 2 seconds and reduced feature datasets developed using Correlation based Feature Selection Algorithm are employed for analysis purpose. Then, five ML algorithms Multilayer Perceptron, Radial Basis Function Neural Network, C4.5 Decision Tree, Bayes Net and Naïve Bayes algorithms are used for internet traffic classification. This analysis shows that C4.5 is an effective ML technique for internet traffic classification provided packet capture duration and number of features characterizing each sample should be minimum.

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Présentation de l'éditeur

With rapid growth of internet traffic over last few years, the area of internet traffic classification becomes very significant for various ISPs. Now days, traditional internet traffic classification techniques such as port number and payload based techniques are seldom used because of use of dynamic port number instead of well-known port number in packet headers and various cryptographic techniques used to encrypt packet payload. Current trends are use of machine learning techniques for internet traffic classification. In this research work, downloaded internet traffic dataset, self-developed internet traffic datasets for packet capture duration of 2 minute and 2 seconds and reduced feature datasets developed using Correlation based Feature Selection Algorithm are employed for analysis purpose. Then, five ML algorithms Multilayer Perceptron, Radial Basis Function Neural Network, C4.5 Decision Tree, Bayes Net and Naïve Bayes algorithms are used for internet traffic classification. This analysis shows that C4.5 is an effective ML technique for internet traffic classification provided packet capture duration and number of features characterizing each sample should be minimum.

Biographie de l'auteur

Kuldeep Singh received B. Tech degree in Electronics & Communication Engineering in 2009 from Punjab Technical University, Jalandhar, Punjab, India and M.E. degree in Electronics & Communication Engineering from Panjab University, Chandigarh, India. He has done his research work under the supervision of Mr. Sunil Agrawal, PU, Chandigarh, India.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
3846595616
ISBN 13 
9783846595619
Reliure
Broché
Nombre de pages
100
Coordonnées du fabricant
non disponible
Personne responsable
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