Sophisticated Machine Learning Methods for Reliable Network Traffic Data Categorization Models

Abstract

Network traffic classification is critical for efficient network management, resource optimization, and security enhancement. The complexity of modern traffic patterns, driven by increasing user demands and diverse applications, poses challenges for traditional methods. This study explores advanced machine learning techniques to address these challenges, focusing on precise classification to improve network performance and detect anomalies effectively. The main key with machine learning such as Support Vector Machines (SVM), K-Nearest Neighbours (KNN), and Logistic Regression were evaluated for their categorization of the capabilities. SVM achieved an accuracy of 99.30%, while KNN and Logistic Regression excelled with accuracies of 99.92%. These results highlight the robustness and adaptability of these models to dynamic and complex network traffic scenarios. Accurate traffic classification facilitates informed decision-making in bandwidth allocation, congestion control, and service prioritization. Furthermore, these scalable models can adapt to evolving network environments and support data-intensive applications. This research demonstrates the transformative potential of machine learning in advancing network operations, offering a foundation for future innovations in intelligent, efficient, and secure network management.