Advanced Machine Learning Techniques for Accurate Network Traffic Classification models

Abstract

Objectives:

The objective of this study is to enhance network performance and security through efficient network traffic classification. The increasing complexity of network traffic, driven by rising user demands and heterogeneous applications, presents challenges for traditional classification methods. This research aims to explore advanced machine learning techniques to improve the accuracy and efficiency of network traffic classification, leading to better resource utilization, congestion reduction, and improved decision-making in network administration.

Methods:

The study employed advanced machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression for network traffic classification. Network traffic data was preprocessed to remove noise and enhance feature quality. The models were trained and tested using a balanced dataset to ensure robustness and generalization. Performance evaluation was conducted using precision as the primary metric. SVM, KNN, and Logistic Regression were tested under varying network conditions to assess their classification efficiency and adaptability.

Results:

The results demonstrated high classification precision across the models. SVM achieved a precision rate of 99.30%, while KNN and Logistic Regression achieved precision rates of 99.92% each. The high accuracy rates reflect the scalability and adaptability of these models in handling complex and dynamic network traffic patterns. The models effectively distinguished between normal and anomalous traffic, enabling improved network resource management and enhanced security.

Conclusions:

The study highlights the potential of machine learning in improving network traffic classification and management. The high precision rates achieved by the models demonstrate their capability to handle complex traffic patterns and adapt to changing network conditions. The research establishes a foundation for future work on intelligent and secure network administration using machine learning, offering a scalable and efficient approach to network resource optimization and anomaly detection