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Cloud security situation prediction method based on grey wolf optimization and BP neural network

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Aiming at the accuracy and error correction of cloud security situation prediction,a cloud security situation prediction method based on grey wolf optimization(GWO)and back propagation(BP)neural network is proposed.Firstly,the adaptive disturbance convergence factor is used to improve the GWO algorithm,so as to improve the convergence speed and accuracy of the algorithm.The Chebyshev chaotic mapping is introduced into the position update formula of GWO algorithm,which is used to select the features of the cloud security situation prediction data and optimize the parameters of the BP neural network prediction model to minimize the prediction output error.Then,the initial weights and thresholds of BP neural network are modified by the improved GWO algorithm to increase the learning efficiency and accuracy of BP neural network.Finally,the real data sets of Tencent cloud platform are predicted.The simulation results show that the proposed method has lower mean square error(MSE)and mean absolute error(MAE)compared with BP neural network,BP neural network based on genetic algorithm(GA-BP),BP neural network based on particle swarm optimization(PSO-BP)and BP neural network based on GWO algorithm(GWO-BP).The proposed method has better stability,robustness and prediction accuracy.

cloud securitysituation predictiongrey wolf optimizationfeature selection

Zhao Guosheng、Liu Dongmei、Wang Jian

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College of Computer Science and Information Engineering,Harbin Normal University,Harbin 150025,China

School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China

This work was supported by the National Natural Science Foundation of ChinaThis work was supported by the National Natural Science Foundation of Chinaand the Natural Science Foundation of Heilongjiang Province of China

6120245861403109LH2020F034

2020

10.19682/j.cnki.1005-8885.2020.0044

中国邮电高校学报(英文版)
北京邮电大学

中国邮电高校学报(英文版)

CSCDEI
影响因子：0.419
ISSN：1005-8885
年,卷(期)：2020.27(6)
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