Internet Traffic Classification through Supervised Learning: Exploring Machine Learning Techniques
The increasing complexity and volume of internet traffic have led researchers to explore machine learning as an effective approach for traffic classification. By integrating intelligence into network processes, machine learning enhances network management and optimization. This study investigates four supervised learning techniques—Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT)—to forecast network traffic categorization. Through a comparative analysis, we evaluate the performance of these algorithms in terms of accuracy, precision, recall, and computational efficiency using a standardized dataset. The results demonstrate that while each algorithm has its strengths and weaknesses, our findings indicate that Random Forest outperforms the other algorithms in most metrics, providing valuable insights for future applications in network management. This study provides valuable insights into the applicability of these algorithms for real-time internet traffic management.
