Publication Type : Conference Paper
Publisher : Proceedings of the 2015 Seventh International Conference on Computational Intelligence, Modelling and Simulation (CIMSim2015)
Source : Proceedings of the 2015 Seventh International Conference on Computational Intelligence, Modelling and Simulation (CIMSim2015), IEEE Computer Society (2015)
Url : http://ieeexplore.ieee.org/document/7579708/
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Year : 2015
Abstract : Peer-to-Peer (P2P) traffic shows a rapid growth in recent times. For efficient malware detection and network traffic management P2P network traffic classification is essential. The existing P2P traffic classification methods includes port-based, signature-based, pattern-based, and statistics based methods. However, none of these methods proved to be effective for the traffic classification in terms of the classification accuracy. This paper proposes a novel classification technique which classifies the internet traffic into P2P and non-P2P traffic with more accuracy and less computational overhead. The proposed classifier is the flow based classifier, that analyses the behavioural patterns utilizing the correlation metric algorithm. The proposed classifier is analyzed for its performance and the results are encouraging.
Cite this Research Publication :
L. M. Nair and Dr. Sajeev G. P., “Internet Traffic Classification by Aggregating Correlated Decision Tree Classifier”, in Proceedings of the 2015 Seventh International Conference on Computational Intelligence, Modelling and Simulation (CIMSim2015), 2015.