Network security situational level prediction based on a double-feedback Elman model

Abstract

Network Security Situational Awareness (NSSA) is an important element in network security research. Predicting network security situational level can help grasp the network security situation. This study mainly focuses on the double-feedback Elman model. Firstly, NSSA was briefly introduced. Then, relevant indicators were selected to establish a security situational indicator system. A back-propagation neural network (BPNN) model was designed to evaluate the situational value. A dual-feedback Elman model was used to predict the future situational level. The actual network environment was built to conduct experiments. The results showed that the evaluation results of only three samples obtained by the BPNN model did not match the actual situation, with an accuracy of 90%, and the prediction results of only four samples obtained by the dual-feedback Elman model did not match the actual situation, with an accuracy of 96.67%. The experimental results verify the reliability of the network security situational level prediction method designed in this study. The NSSA method can be promoted and applied in practice.