Network security situation prediction model based on grey wolf optimization algorithm to optimize support vector machine

Abstract

When making network security situation prediction, researchers usually use historical network security situation values to make predictions. Such predictive models often do not perform well on complex networks with multi-source inputs. Aiming at the problem that the prediction accuracy of the existing prediction models is not high in complex networks, a network security situation prediction model based on grey wolf optimization algorithm to optimize support vector machine is proposed. In this paper, the situation elements are first accumulated, and then the support vector machine is used to independently predict different situation elements in the network, and finally the predicted values are fused into the network security situation value. The parameters of the support vector machine are determined using the grey wolf optimization algorithm. The simulation results show that the model has a good prediction effect.