The classification of Internet traffic has come to the forefront in recent times as organization of network traffic is necessitated by the increasing use of the internet and limited bandwidth. Also, network traffic classification finds its application in network security and for Qos (Quality of service). In this report, a certain number of flow features have been used as a basis for classifying the network traffic into various applications that run on the network as the classes. These flow features (13 in all) were extracted using a Perl script after capturing traffic using Wire shark. Seven network applications were chosen as the classes, visually, ftp, www, p2p, NetBIOS, dns, mail and telnet for classification. The machine algorithms that have been used for classification are Artificial Neural Network (ANN) and Support Vector Machine (SVM). These algorithms were used while designing a classification simulation model in WEKA in which Multilayer Perceptron (MLP) and sequential Minimal Optimization (SMO) function was used respectively. Furthermore, comparisons on the performance of these algorithms have been carried out for arriving at the better network traffic classification..

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Support Vector Machine Artificial Neural Network Machine learning algorithm Quality of Service Security perspective Network Traffic Classification