Identification of Network Traffic Based on Radial Basis Function Neural Network

Abstract

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A method of network traffic identification based on RBF (Radial Basis Function) neural network is proposed by analysis of the current status of the network environment. By using the public data set and the real-time traffic for a combination of supervised learning, this method constructs a reasonable training set and testing set to experiment and implement the network traffic identification based on RBF neural. The experiments prove the identification method in the application of network traffic has the characteristics of high accuracy, low complexity and high recognition efficiency, and the practical feasibility in real-time traffic identification.