Abstract
The evolution of the internet into a large, complex service-based network has posed tremendous challenges for network monitoring and control in terms of how to collect massive volumes of data, in addition to the accurate classification of new emerging applications, such as peer-to-peer networks, streaming content and online games. In this work, machine learning algorithms are used for the classification of traffic into their corresponding applications. Furthermore, this research uses our customized training data set collected from the three institutions' campuses. The effect on the size of the training data set has been considered before examining the accuracy of various classification algorithms and selecting the best from a large amount of data traffic in the network, which has led to delays in performance; therefore, to solve this problem we suggested a distinct approach using multiple neural networks with the feature selection in order to predict and identify known and unknown applications. By applying the proposed method, we get excellent accuracy in the classification of data traffic in the network of up to 99.11%, which leads to improved data traffic in the network and avoids delays. (C) 2020 The Authors. Published by IASE.