Research on Prediction and Response Algorithm of Network Security Events Driven by Security Situation

This study focuses on developing effective methods for predicting and responding to network security threats using advanced computational techniques. Our approach begins with constructing a detailed network security assessment model that evaluates network assets based on their confidentiality, integrity, and availability. This model enables a thorough understanding of the network security landscape. We then apply predictive analytics to uncover the complex patterns in network security events, specifically examining how sample size and concurrent user count affect the performance of prediction models. Our findings suggest that increasing sample size improves both accuracy and specificity of these models, while the effect of concurrent user count on accuracy is minimal, though it does impact specificity. In addition, we develop and employ sophisticated computational techniques to enhance real-time response strategies. By analyzing the predictive model results, we aim to create adaptive response algorithms that can adjust to the evolving nature of network threats. This approach provides actionable insights for optimizing response strategies and improving overall network security. The study concludes with recommendations for integrating these predictive and response methods into practical applications, offering valuable insights for enhancing network security through advanced computational approaches.