Application of Bayesian Network and Support Vector Machine in Evaluation and Prediction of Network Security Situation

With the development of economy, computers have entered thousands of households, and the openness, sharing and interconnection of networks are getting higher and higher, which brings great convenience to people’s lives and work. However, various network security incidents continue to occur, and the network security is facing great challenges. Therefore, network security situational awareness technology came into being, aiming to accurately predict the future security situation by identifying and selecting the key factors that affect network security. This process is essentially a deep understanding and grasp of the network security status. In this paper, the potential threats and hidden dangers of network security are comprehensively evaluated by combining Bayesian network model with support vector machine technology. Bayesian network relies on its powerful analysis capabilities to deeply analyze network operation data and situation indicators, so as to accurately judge whether the network status reaches the security threshold. Support vector machine, with its many advantages such as strong universality, simple calculation, efficient processing and perfect theoretical framework, stands out among many network security situation prediction algorithms and becomes a mature and efficient solution. The application of this method plays an extremely important role in significantly improving the accuracy of network security situation prediction.