Application of machine learning algorithm and data evaluation in computer network security situation awareness technology.

The article focuses on the development of a network security (NS) situational awareness model based on machine learning (ML) and data analysis technologies to address increasingly complex cyber threats such as viruses, trojans, and Denial-of-Service (DOS) attacks. It outlines the architecture and key components of NS situational awareness, including data collection, preprocessing, situation analysis, prediction, and visualization, emphasizing the integration of ML algorithms like support vector machines and neural networks for improved detection and prediction. The proposed model demonstrated a 7.18% higher detection efficiency compared to traditional NS situational awareness models, with enhanced accuracy, reduced error rates, and better processing performance through parallel computing. This approach aims to provide real-time, dynamic monitoring and early warning capabilities to support network security managers in making informed decisions and implementing timely defenses.