Security Evaluation of Situational Awareness in Cyberspace Based on Artificial Neural Network-Back Propagation

Abstract

Computer network security has become increasingly controversial among many businesses as a result of the rise in cyber threats. Artificial neural network (ANN) is mature research in this field, whereas the traditional algorithm is slower, in feedback error, and has the disadvantage of easy convergence to local extreme value. To guide against these threats, in this paper, ANN-back propagation (BP) algorithm is used to establish the relationship between the level of cyber security situational awareness (CSA) and the perceptual parameters and quantitatively evaluate the situational awareness. This study established the ANN-back propagation (BP) to make the relationship between the level of cyber security situational awareness (CSA) and the perceptual parameters, which evaluates situational awareness. The ANN-BP with variable step size learning strategy and simulated annealing method is used for optimization to build a virtual network environment. The proposed model offers better precision, improved sensitivity, and higher (0.987%) accuracy.