Abstract

The rapid expansion of large-scale communication systems, including 5G networks, cloud infrastructures, and enterprise networks, has resulted in exponential growth of network traffic. Accurate network traffic classification is essential for network management, quality of service (QoS) enforcement, intrusion detection, and anomaly mitigation. Traditional traffic classification approaches, such as port-based and deep packet inspection (DPI), face challenges in scalability, encrypted traffic handling, and real-time processing. This paper explores machine learning-driven approaches for network traffic classification in large-scale communication systems. Supervised, unsupervised, and deep learning methods are leveraged to automatically analyze traffic features and categorize flows into applications, protocols, or service types. Feature engineering, dimensionality reduction, and model optimization techniques are employed to improve classification accuracy and computational efficiency. Experimental evaluation on large datasets demonstrates that machine learning-based classifiers outperform traditional methods in terms of accuracy, adaptability to encrypted traffic, and real-time processing capabilities. The study highlights the importance of combining statistical feature extraction, flow-based analysis, and adaptive learning algorithms to enhance traffic classification performance in dynamic and heterogeneous network environments. This research provides a foundation for deploying intelligent network monitoring, congestion management, and security enforcement mechanisms in modern large-scale communication systems