ANALYTICAL REVIEW OF MODELS AND SYSTEMS FOR NETWORK TRAFFIC CLASSIFICATION

This article presents a comprehensive analytical review of network traffic classification models and systems, essential for managing the complexities of modern network environments. The study covers traditional and advanced methods, including statistical approaches, machine learning, and deep learning techniques, highlighting their strengths and limitations. It also explores both commercial and open-source systems, offering insights into their practical applications and effectiveness. The rapid evolution of network technologies has significantly enhanced global data exchange and connectivity but has also introduced new challenges in managing and securing complex network environments. As networks expand and grow more heterogeneous, the ability to classify and manage network traffic efficiently becomes critical for optimizing network performance, ensuring security, and supporting operational continuity. Network traffic classification is an essential function that enables network administrators to apply appropriate policies, detect anomalies, and prevent malicious activities. Traditional classification methods, such as payload-based detection and port-based classification, are increas