Comparative study on different neural networks for network security situation prediction

This article mainly studied the performance of different neural networks in the processing network security situation prediction (NSSP). Radial basis function (RBF) and back propagation neural network (BPNN) models were optimized by particle swarm optimization (PSO) algorithm and seeker optimization algorithm (SOA), respectively. Then the PSO-RBF model and SOA-BPNN model were obtained, and comparative experiments were carried out on CNCERT/CC data set. The results suggested that the improved models were more accurate in predicting the situation value compared with RBF and BPNNmodels; the PSO-RBF mode had three prediction errors, with 0.05 mean square error (MSE) and 0.05 mean absolute error (MAE), and the SOA-BPNNmodel had six prediction errors, with 0.2 MSE and 0.13 MAE, which showed that the PSO-RBF model had better performance. The experimental results show that the PSO-RBF model has an excellent performance in processing NSSP and can be promoted and applied in practice.