Title: A traffic identification based on PSO-RBF neural network in peer-to-peer network

Authors: Yong Chen; Huiqin Ji; Huanlin Liu; Longzhao Sun

Addresses: Key Laboratory of Industrial Internet of Things and Network Control, MOE, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China ' Key Laboratory of Industrial Internet of Things and Network Control, MOE, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China ' Key Laboratory of Optical Fiber Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China ' Key Laboratory of Industrial Internet of Things and Network Control, MOE, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

Abstract: To identify and control the peer-to-peer (P2P) traffic accurately, this paper proposes a novel classification method of peer-to-peer network traffic identification based on machine learning. The method is based on the conventional machine learning to identify the network traffic, in the data specimen collection stage, adding the particle swarm optimisation algorithm to collect the data. In the classification tool building stage, a radial basis function neural network is used, which is very suitable for specify the data category. In the experiment, collecting three kinds of typical traffic (P2P traffic, HTTP traffic, and game traffic) on the current internet, identification and classification, results show that the method has a higher precision rate and recall rate (two evaluation indexes of three kinds of flow rate with more than 90%). The use of particle swarm optimisation feature selection algorithm reduces the training time. On the whole, the method has good classification effect.

Keywords: machine learning; traffic identification; RBF neural networks; radial basis function; network traffic identification; particle swarm optimisation; PSO; peer-to-peer networks; P2P networks; classification.

DOI: 10.1504/IJCSE.2016.078444

International Journal of Computational Science and Engineering, 2016 Vol.13 No.2, pp.158 - 164

Received: 19 Jul 2014
Accepted: 13 Nov 2014
Published online: 19 Aug 2016 *

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