Binary classification of Internet Traffic for Educational Institutions - Couverture souple

Kaur, Jaspreet; Agrawal, Sunil

 
9783659301490: Binary classification of Internet Traffic for Educational Institutions
Couverture souple
ISBN 10 : 3659301493 ISBN 13 : 9783659301490
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

The infinite number of websites in the internet world can be classified into a number of categories depending on the purpose for classification.In educational institutions, it is required that the internet facility be used for academic purposes only. This can happen through accurate classification of network traffic into two classes, educational websites and non-educational websites. In educational institutes, for the optimum use of network resources the use of non-educational websites should be banned. In our research work, we explore the use of ML (machine learning) algorithms for classification of internet traffic into two classes. We discuss the basis for differentiating the traffic into these two classes, and investigate the impact of flexibility in making such differentiation on the performance of selected five ML algorithms. We also investigate the impact of class error in data set and propose a generalized feature subset which is robust against all these impacts. Results show that Bayes Net algorithm performs consistently for intended classification of internet traffic in terms of classification accuracy, training time of classifiers, recall and precision.

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Présentation de l'éditeur

The infinite number of websites in the internet world can be classified into a number of categories depending on the purpose for classification.In educational institutions, it is required that the internet facility be used for academic purposes only. This can happen through accurate classification of network traffic into two classes, educational websites and non-educational websites. In educational institutes, for the optimum use of network resources the use of non-educational websites should be banned. In our research work, we explore the use of ML (machine learning) algorithms for classification of internet traffic into two classes. We discuss the basis for differentiating the traffic into these two classes, and investigate the impact of flexibility in making such differentiation on the performance of selected five ML algorithms. We also investigate the impact of class error in data set and propose a generalized feature subset which is robust against all these impacts. Results show that Bayes Net algorithm performs consistently for intended classification of internet traffic in terms of classification accuracy, training time of classifiers, recall and precision.

Biographie de l'auteur

I, Jaspreet Kaur am currently working as an Assistant Professor at CGC Landran, Punjab, India. I did my ME from UIET, PU Chandigarh, India. This work would not have been possible without the kind support of my Guide Mr. Sunil Agrawal and my parents Mom, Dad, brother, sister and friends.

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
2012
Langue
anglais
ISBN 10 
3659301493
ISBN 13 
9783659301490
Reliure
Broché
Nombre de pages
96
Coordonnées du fabricant
non disponible
Personne responsable
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