Network Security Situation Awareness Based on Particle Swarm Optimization of Gated Cycle Unit

The network security situation awareness method based on particle swarm optimization algorithm is studied in the paper to optimize the gating cycle unit. For the GRU model is easy to fall into local optimum and other shortcomings, the particle swarm algorithm is used to improve it. The differential evolution algorithm is used to improve the particle diversity of the particle swarm algorithm and improve the global optimal solution search ability of the algorithm. The differential evolution improved particle swarm optimization algorithm is used to optimize the weight coefficients of GRU neural network to improve the prediction accuracy of the model. The public data sets are used to verify the proposed model, and the experimental results show that the prediction results of the proposed model have good accuracy and reliable prediction accuracy. Compared with the traditional model and the unoptimized network model, the experimental results show that the prediction accuracy of the proposed model is much higher than that of other models, which further illustrates the effectiveness and feasibility of the proposed model.