A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm

It is important to establish the forecasting model of the network security situation. But the network security situation cannot be observed directly and can only be measured by other observable data. In this paper the network security situation is considered as a hidden behavior. In order to predict the hidden behavior, some methods have been proposed. However, these methods cannot use the hybrid information that includes qualitative knowledge and quantitative data. As such, a forecasting model of network security situation is proposed on the basis of the hidden belief rule base (BRB) model when the inputs are multidimensional. The initial parameters of the hidden BRB model given by experts may be subjective and inaccurate. In order to train the parameters, a revised covariance matrix adaption evolution strategy (CMA-ES) algorithm is further developed by adding a modified operator. The revised CMA-ES algorithm can optimize the parameters of the hidden BRB model effectively. The case study shows that compared with other methods, the proposed hidden BRB model and the revised CMA-ES algorithm can predict the network security situation effectively to improve the forecasting precision by making full use of qualitative knowledge.