Network Security Situation Prediction Model Based on EMD and ELPSO Optimized BiGRU Neural Network

The traditional network security situation prediction method depends on the accuracy of historical situation values, and there are correlations and differences in importance among various network security factors. To solve the above problems, a combined forecasting model based on Empirical Mode Decomposition and improved Particle Swarm Optimization (ELPSO) to optimize BiGRU neural network (EMD-ELPSO-BiGRU) is proposed. Firstly, the model decomposes the network security situation data sequence into a series of intrinsic modal components by empirical mode decomposition; Then, the prediction model of the BiGRU neural network is established for each modal component, and an improved Particle Swarm Optimization Algorithm (ELPSO) is proposed to optimize the super parameters of BiGRU neural network. Finally, the prediction results of each modal component are superimposed to obtain the final prediction value of the network security situation. In the experiment, on the one hand, ELPSO is compared with other particle swarm optimization algorithms, and the results show that ELPSO has better optimization performance; On the other hand, through simulation test and comparison between EMD-ELPSO-BiGRU and other traditional forecasting methods, the results show that the established combined forecasting model has higher forecasting accuracy.