Feature-Centric Framework for Network Traffic Management and Optimization

Abstract

While complex network development and dynamic traffic are still developing, there has been great optimization and management of network traffic. This paper will develop a feature-based approach based on intelligent selection and reduction to improve traffic flow management, optimization of resource utilization, and enhancement of network security. While methods of statistical correlation and clustering retain only the relevant features in the system, irrelevant ones are filtered out and operational efficiency is optimized along with the decision-making process. Advanced correlation algorithms are proposed in a cross-selection set, such as Spearman and Kendall, besides dendrogram-based clustering analysis. This makes the solution robust for designing and monitoring modern network environments. It can be seen from the experimental results that the proposed framework can remove redundancy and enhance the feature interpretability, scalability, and adaptability to domain-specific network requirements. Furthermore, the framework proves its efficiency in capturing nonlinear feature relationships, making it robust for real-world network applications.