ANALYTICAL REVIEW OF MODELS AND SYSTEMS FOR NETWORK TRAFFIC CLASSIFICATION

Abstract

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 increasingly unreliable due to the rise of encrypted traffic and evolving network protocols, which obscure important traffic details. Therefore, it is necessary to explore advanced approaches such as machine learning, deep learning, and transfer learning. These methods can handle large volumes of data and adapt to new traffic types, improving accuracy and efficiency. This paper presents a comprehensive review of existing models and systems for network traffic classification, including both commercial and open-source solutions. The study covers a range of techniques from traditional statistical methods to advanced machine learning and artificial intelligence (AI)-based techniques. Special attention is given to key performance indicators (KPIs) such as latency, packet loss, jitter, throughput, and bandwidth utilization, which are vital for effective network monitoring and optimization. In light of increasing encryption and evolving cyber threats, the paper emphasizes the importance of adaptive systems, real-time analysis, and the integration of AI and machine learning technologies. The review concludes by identifying future research directions and emerging trends, crucial for developing the next generation of traffic classification systems.