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

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. © 2020, Beijing University of Posts and Telecommunications. All rights reserved.