Abstract: There is a data storage covert channel between the client and the server,and the network traffic on the channel needs to be accurately predicted,which can avoid network congestion and improve network traffic scheduling and management ability.In the traditional method,the linear time series analysis method is used to predict the network traffic,which can not accurately reflect the nonlinear characteristic information,and the prediction accuracy is not high.A network traffic prediction algorithm was proposed based on nonlinear time series analysis and vector space reconstruction.The phase randomization process makes the network traffic data discrete analysis,and the network traffic time series analysis model is decomposed into the statistics of multiple nonlinear components.The self correlation function is used to obtain the vector space reconstruction time delay,and the mutual information minimum embedding dimension algorithm is used to obtain vector space embedding dimension of network flow sequence,which realizes the vector space reconstruction of flow sequence.In the high dimensional vector space,the high order spectral characteristics of the network traffic are extracted,the accurate prediction of the network traffic is realized.Simulation results show that the proposed algorithm can effectively simulate the nonlinear state characteristics of the traffic sequence,the dynamic tracking performance of the traffic state is better,and the prediction error is lower than the conventional method.

Key words: Network traffic,Prediction,Vector space reconstruction,Nonlinear