Research on gray correlation analysis and situation prediction of network information security

Abstract

AbstractIn order to further improve the accuracy and efficiency of network information security situation prediction, this study used the dynamic equal-dimensional method based on gray correlation analysis to improve the GM (1, N) model and carried out an experiment on the designed network security situation prediction (NSSP) model in a simulated network environment. It was found that the predicted result of the improved GM (1, N) model was closer to the actual value. Taking the 11th hour as an example, the predicted value of the improved GM (1, N) model was 28.1524, which was only 0.8983 larger than the actual value; compared with neural network and Markov models, the error of the improved GM (1, N) model was smaller: the average error was only 2.3811, which was 67.88% and 70.31% smaller than the other two models. The improved GM (1, N) model had a time complexity that was 49.99% and 39.53% lower than neural network and Markov models; thus, it had high computational efficiency. The experimental results verify the effectiveness of the improved GM (1, N) model in solving the NSSP problem. The improved GM (1, N) model can be further promoted and applied in practice and deployed in the network of schools and enterprises to achieve network information security.