Network security threat detection model based on large-scale multi-source data analysis and perception fusion

Network security threat detection model based on large-scale multi-source data analysis and perception fusion

연구 분야: Safety

학회: Neural Computing and Applications

To solve the problem of insufficient adaptability of traditional network security threat detection model, this paper constructs an integrated framework of comprehensive multi-source analysis, perception and fusion, combining network traffic, logs, user behavior and other information sources, using deep learning and information retrieval strategies to effectively perform data preprocessing and feature optimization. The experimental results show that the efficiency of the model reaches 97.5%, and the accuracy and flexibility of the model exceed the traditional single-source data early warning method, which provides strong technical support for network security. To sum up, the goal of this document is to connect the evolving dangers with the demand for sophisticated, adaptive defense strategies that safeguard essential digital infrastructure.