Open AccessClassifying Internet Traffic using An Efficient Classifier
Author(s) -
Haitham A. Jamil,
Bushra M Ali,
Hind G. Abdelrahim,
Azza O. Awad
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b2041.098319
Subject(s) - the internet , computer science , traffic classification , internet traffic , internet traffic engineering , classifier (uml) , computer network , computation , data mining , machine learning , artificial intelligence , world wide web , algorithm
The new development in the architecture of Internet has increased internet traffic. The introduction of Peer to Peer (P2P) applications are affecting the performance of traditional internet applications. Network optimization is used to monitor and manage the internet traffic and improve the performance of internet applications. The existing optimizations methods are not able to provide better management for networks. Machine learning (ML) is one of the familiar techniques to handle the internet traffic. It is used to identify and reduce the traffic. The lack of relevant datasets have reduced the performance of ML techniques in classification of internet traffic. The aim of the research is to develop a hybrid classifier to classify the internet traffic data and mitigate the traffic. The proposed method is deployed in the classification of traffic traces of University Technology Malaysia. The method has produced an accuracy of 98.3% with less computation time