Network security situation prediction based on combining associated entropy and deep recurrent neural network

The existing network security situation prediction algorithm cannot deal with the subjective attitude deviation of multi-experts, and the traditional sequential machine learning model cannot deal with the problem of deviation accumulation in time period. In this paper, an algorithm of network security situation prediction combining entropy correlation and deep time series network model is proposed. First, the expert fuzzy evaluation index is constructed by trigonometric fuzzy function, and the modified weighted dempster-shafer (DS) evidence reasoning correction index is used, then the loss and possibility matrix features are created, and finally the information security is evaluated by deep time series network. The simulation experiments are carried out on MIT dataset. The experiments analyze whether the features can cope with multi-expert conflicts, and evaluate the accuracy, robustness, and time efficiency of the algorithm. The experimental results show that the algorithm proposed in this paper has stronger fuzzy evaluation ability, stronger ability to deal with conflict opinions among experts, more accurate prediction of network security situation in time sequence, and higher robustness, but the efficiency of the algorithm has been maintained.