Network Traffic Classification via Kernel Based Extreme Learning Machine

Fatih Ertam; Engin Avcı

doi:https://doi.org/10.18201/ijisae.267522

Article Details

Network Traffic Classification via Kernel Based Extreme Learning Machine

Authors:

Fatih Ertam | Engin Avcı

Publisher:

İsmail SARITAŞ

Main Topics:

Applied physics

Journal:

International Journal of Intelligent Systems and Applications in Engineering 2016 Vol.4 Issue Special Issue-1, pp.109-113

Document Type:

Article

Publication Date:

12/2016

Document Language:

Turkish

Document ID (Org No.):

2000817497

Frequency:

Quarterly

eISSN:

2147-6799

DOI:

https://doi.org/10.18201/ijisae.267522

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.

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Article Details

Network Traffic Classification via Kernel Based Extreme Learning Machine

Authors: Fatih Ertam | Engin Avcı

Publisher: İsmail SARITAŞ

Main Topics: Applied physics

Journal: International Journal of Intelligent Systems and Applications in Engineering 2016 Vol.4 Issue Special Issue-1, pp.109-113

Document Type: Article

Publication Date: 12/2016

Document Language: Turkish

Document ID (Org No.): 2000817497

Frequency: Quarterly

eISSN: 2147-6799

DOI: https://doi.org/10.18201/ijisae.267522

Issues

Authors:

Fatih Ertam | Engin Avcı

Publisher:

İsmail SARITAŞ

Main Topics:

Applied physics

Journal:

International Journal of Intelligent Systems and Applications in Engineering 2016 Vol.4 Issue Special Issue-1, pp.109-113

Document Type:

Article

Publication Date:

12/2016

Document Language:

Turkish

Document ID (Org No.):

2000817497

Frequency:

Quarterly

eISSN:

2147-6799

DOI:

https://doi.org/10.18201/ijisae.267522