Internet Traffic Classification for Educational Institutions Using Machine Learning
Jaspreet Kaur, Sunil Agrawal, Balwinder Singh Sohi · International Journal of Intelligent Systems and Applications · 2012
In recent times machine learning algorithms are used for internet traffic classification.The infin ite number of websites in the internet world can be classified into different categories in d ifferent ways.In educational institutions, these websites can be classified into two categories, educational websites and noneducational websites.Educational websites are used to acquire knowledge, to exp lore educational topics wh ile the non-educational websites are used for entertainment and to keep in touch with people.In case of blocking these non-educational websites students use proxy websites to unblock them.Therefo re, in educational institutes for the optimu m use of network resources the use of non-educational and proxy websites should be banned.In this paper, we use five ML classifiers Naï ve Bayes, RBF, C4.5, M LP and Bayes Net to classify the educational and non-educational websites.Results show that Bayes Net gives best performance in both full feature and reduced feature data sets for intended classification of internet traffic in terms o f classification accuracy, recall and precision values as compared to other classifiers.