The infinite number of websites in the internet world can be classified into different categories in different ways. But if we talk about the educational institutions, websites can be classified into two categories, educational websites and non-educational websites. Educational websites are those websites which are used by the students to acquire knowledge, to explore educational topics, for the research work etc. The non-educational websites are used for entertainment and to keep in touch with people and to get to know more people. In educational institutes for the optimum use of network resources and for the welfare of the students, the use of non-educational websites should be banned while only the educational websites should be allowed to access. Recent trends are use of ML (machine learning) algorithms for internet traffic classification. In this paper, we use three ML classifiers Bayes Net, C4.5 and Radial basis function (RBF) neural network to classify the educational and non-educational websites and compare their performances. Results show that Bayes Net gives best performance for intended classification of internet traffic in terms of classification accuracy, training time of classifiers, recall and precision.

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Rbf C4.5 Bayes Net Features