Machine Learning in Network Traffic Analysis: Classification, Optimization, and Security

Abstract: With the rapid expansion of digital networks, ensuring efficient and secure network traffic management has become a significant challenge. Traditional rule-based approaches struggle to handle evolving traffic patterns, particularly with the increasing use of encryption. Machine learning (ML) has emerged as a powerful alternative, providing enhanced capabilities for traffic classification, anomaly detection, and optimization. This paper presents a comprehensive review of ML-based techniques, including supervised learning, unsupervised learning, deep learning, and graph-based learning. Key challenges such as data imbalance, real-time processing, and computational overhead are explored. The study consolidates findings from multiple research papers, emphasizing the role of AI-driven models in improving cybersecurity, traffic prediction, and quality of service (QoS). Future research directions include hybrid models, federated learning, and the integration of ML with emerging networking paradigms such as Software-Defined Networking (SDN) and 5G