Abstract
With the rapid development of the information society, security threats in cyberspace are increasing day by day, posing severe challenges to national infrastructure, commercial operations, and even personal privacy. At present, the research of network security situational awareness and risk assessment model is faced with critical problems, such as significant demand for prior knowledge, high complexity of inference algorithm, insufficient dynamic adaptability, and inaccurate identification of risk categories. In view of this, this study proposes a new network security situational awareness and risk assessment model based on the Bayesian network, aiming to achieve timely early warning and accurate prediction of network threats through probability statistics methods. By comprehensively considering various heterogeneous data sources such as network traffic anomalies, system log anomalies, and external threat intelligence, we built a sizeable Bayesian network covering thousands of nodes and hundreds of thousands of edges to describe the occurrence mechanism and evolution path of network security incidents. Empirical research shows that the optimized model has an accuracy rate of 92%, a recall rate of 89%, and an F1 score of 90.5% on the test dataset, which is significantly better than the existing rule-based and machine-learning methods, especially when dealing with low-frequency threats with apparent long-tail effects, showing more robust adaptability and prediction accuracy. By dynamically monitoring the changing trend of network activities, we can identify potential risk points in advance, help take proactive protective measures before security threats occur, and effectively reduce economic losses caused by network intrusions. This study not only provides a brand-new theoretical framework and technical means for network security situational awareness and risk assessment but also opens up broad prospects for subsequent research directions and application scenarios.
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