A network security posture assessment model based on binary semantic analysis

Advancements of modern-day industry and the decision support systems have improved the progress of intelligent applications for performing critical tasks. Decision support system is any system or tool which has the potential or ability to support decision making activities. In this paper, a network security posture assessment model for decision making based on binary semantic analysis is proposed to address the problems of traditional network security. By semantic analysis of pre-compiled script files, the behavioral features of script files are obtained based on abstract syntax trees and the supervised learning of samples is performed using BP neural networks to obtain a detection model that can be used for unknown samples. Different from the existing detection methods based on semantic analysis, the network security posture assessment index system is first established, the weights of the indexes are determined by using the sequential relationship analysis method, and finally the binary semantic analysis method is introduced into the decision matrix to realize the network security posture assessment model. The simulation results of the study show that the introduction of binary semantic analysis and sequential relational analysis significantly improves the accuracy of network security posture assessment, and the proposed WebShell detection method has much high accuracy and recall rate.