Network Security Prevention Based on D-DNNsafe

Abstract

Network security refers to the ability to protect computer networks from unauthorized access, destruction, theft or damage. With the popularization of the Internet and the acceleration of information development, network security has become a very important issue. Network security situational awareness is a new network security protection technology that evaluates the security status of the current network operating environment and predicts the security change trend of the network operating environment in the future period based on the relevant elements in the current network operating environment, with the ultimate purpose of ensuring network security. At present, researches on network security situation approach mainly focus on improving the accuracy of logical judgment, which is difficult to achieve a balance between logical processing and spatio-temporal complexity. In addition, there are some problems such as high complexity and lack of objectivity of the implementation model. Based on this, this paper designs a new network security situation assessment method based on D-S evidence theory. Firstly, aiming at the deficiency of objectivity in traditional single D-S evidence theory, Deep Neural Networks (DNN) is proposed to obtain OP to reduce its subjective dependence. Secondly, in order to solve the problems of low accuracy and low time efficiency when DNN processes massive data, SAE is used to reduce the dimensionality of massive high-dimensional data, and D-DNNsafe network security situation assessment model is constructed.