Prediction of network security situation awareness based on an improved model combined with neural network

Abstract

People always pay attention to the security of the network. This paper mainly analyzed the problem of network security situation prediction (NSSP). The radial basis function neural network was improved by the particle swarm optimization algorithm, and a modified particle swarm optimization‐radial basis function algorithm was obtained, which was used as the prediction model. Then, the data from National Internet Emergency Center were used as the experimental data, and the modified particle swarm optimization‐radial basis function algorithm was compared with radial basis function and particle swarm optimization‐radial basis function algorithms. The results showed that the modified particle swarm optimization‐radial basis function algorithm could achieve convergence in about 50 times of iterations, showing a high calculation efficiency and a short operation time, and the mean absolute percentage error value, mean square error value, and root‐mean‐square error value were small (2.13%, 0.0005, and 0.0224), showing that the algorithm had good prediction performance. The results verify the reliability of the modified particle swarm optimization‐radial basis function algorithm in NSSP, which is conducive to further improve network security.

Enhanced Tab Transformer with Month-Based Binning Missing Data Imputation for Cyber Security Situation Prediction

“…Yuan (2021) [5] developed an improved RBF algorithm and particle swarm optimization (PSO) algorithm, which was successfully applied to NSSP problems and improved the accuracy of the network security situation prediction model, making an important contribution to protecting network security.…”

HKIT-Trans: Huber-KNN and improved tab-transformer model for network security situation prediction

“…Wang verified the advantages of RBF neural network through comparative analysis, realized the optimization of RBF and BPNN models, and proved the reliability of neural network method in security situation prediction, which was conducive to the further application of neural network. [5] designed the improved Radial Basis Function (RBF) algorithm and Particle Swarm Optimization (PSO) algorithm and verified the effectiveness of the algorithm to solve the NSSP problem through experimental analysis, which made a contribution to the better realization of network security. [6] designed a backpropagation neural network (BPNN) model to evaluate the situation value.…”

A Hidden Markov Model-Based Network Security Posture Prediction Model

“…The literature [17] compared several network security posture prediction methods and concluded that the particle swarm optimization (PSO) algorithm optimized radial basis (RBF) neural network has better performance. The literature [18] similarly proposed an improved PSO algorithm to optimize RBF neural networks for situational prediction.…”