Network Traffic Classification via Kernel Based Extreme Learning Machine

Fatih Ertam, Engin Avcı
  • Engin Avcı
    Affiliation not present

Abstract

The classification of data on the internet in order to make internet use more efficient has an important place especially for network administrators managing corporate networks. Studies for the classification of internet traffic have increased recently. By these studies, it is aimed to increase the quality of service on the network, use the network efficiently, create the service packages and offer them to the users. The first classification method used for the classification of the internet traffic was the classification for the use of port numbers. This classification method has already lost its validity although it was an effective and quick method of classification for the first usage times of the internet. Another classification method used for the classification of network traffic is called as load-based classification or deep packet analysis. This approach is based on the principle of classification by identifying signatures on packets flowing on the network. Another method of classification of the internet traffic which is commonly used in our day and has been also selected for this study is the kernel based on extreme learning machine based approaches. In this study, over 95% was achieved accuracies using different activation functions.

Keywords

Machine Learning; Classification; Extreme Learning Machine; Network Classification

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Submitted: 2018-12-21 10:10:59
Published: 2016-12-26 00:00:00
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