Composite Prediction Method of Network Security Situation Based on CEEMD and Time Series Estimation

Weina He

Abstract


Composite prediction method of network security situation based on CEEMD and time series estimation has been proposed. Network security information has been decomposed based on the completeness of CEEMD and low frequency linear part as well as high frequency non-linear part has been attained ARIMA modeling and estimation have been adopted for linear data, adaptive radial basis function prediction has been made to non-linear part and finally reconstruct these two parts to form the final prediction model. Simulation result has shown that compared with other prediction methods, making prediction for the value of network security situation with adoption of the method in this paper can greatly improve the prediction accuracy. Make prediction error mainly concentrates on high frequency component of the first few orders through CEEMD decomposition of original situation value; the adoption of frequency division composite prediction method not only saves the overall training and prediction time, but also decreases prediction error effectively. The prediction result can follow up the changing trend of network security more accurately and provide useful reference to the management of network security.

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References


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