A novel traffic identification approach based on multifractal analysis and combined neural network
An accurate identification of Internet traffic of different applications is highly relevant for a broad range of network management and measurement tasks, including traffic engineering, service differentiation, performance monitoring, and security. Traditional traffic identification approaches have become increasingly inaccurate due to restrictions of port numbers, protocol signatures, traffic encryption, and etc. In this paper, a new traffic identification approach based on multifractal analysis of wavelet energy spectrum and classification of combined neural network models is proposed. The proposed approach is able to achieve the identification of different Internet application traffic by performing classification over the wavelet energy spectrum coefficients that were inferred from the original traffic. Without using any payload information, the proposed approach has more advantages over traditional methods. The experiment results illustrate that the proposed approach has satisfactory identification results.
multifractal analysis wavelet energy spectrum combined neural network models discrete wavelet transform levenbergmarquardt algorithm