Research on Network Security Situational Awareness Prediction Method Based on Intelligent Learning Algorithm
Yue Wu, Jun Tao, Hao He · 2023
With the rapid development of the Internet, the network has become an indispensable part of people's lives and has been widely used in daily life and work, but at the same time, computer technology is also facing great challenges. Hacker attacks, virus attacks and other problems have emerged one after another, and these security risks have brought us a series of serious impacts and hazards. The study of network security posture is important because it can improve network monitoring capabilities, emergency response capabilities and predict the development trend of network security. In order to do a good job in network security defence, based on improving the existing static defence mechanism, network security situational awareness enables efficient analysis of various network security-related data and threat intelligence, while traditional assessment and prediction methods have problems such as being highly subjective, requiring access to a priori knowledge as well as being difficult to process in the face of massive amounts of data and relying too much on human resources when facing a dynamically changing network environment. Therefore, the combination of intelligent learning algorithms in this study allows network security situational awareness to be developed in the direction of intelligence and automation as well. A Bayesian decision model and Class-Attribute Interdependency Maximization (CAIM) discretization algorithm are used for the study, which further improves the network security situational assessment method to a certain extent.