NETWORK TRAFFIC CLASSIFICATION LEVERAGING MACHINE LEARNING ALGORITHMS

Abstract

With the rapid growth of digital networks, managing network traffic efficiently and securely has become increasingly
complex. Traditional rule-based methods often fail to adapt to dynamic traffic patterns, especially with the widespread adoption
of encryption technologies. In this context, machine learning (ML) has emerged as a powerful solution, offering advanced
capabilities in traffic classification, anomaly detection, and network optimization.
This paper provides a detailed review of ML-based approaches, including supervised learning, unsupervised learning, deep
learning, and graph-based techniques. It examines how these methods contribute to improved network performance and
security. Additionally, key challenges such as data imbalance, real-time implementation constraints, and computational
complexity are discussed in detail. The study brings together insights from various research works, highlighting the growing
importance of AI-driven models in enhancing cybersecurity, predicting traffic behavior, and maintaining quality of service
(QoS). It also identifies future research opportunities, including the development of hybrid learning models, the adoption of
federated learning, and the integration of ML techniques with modern networking frameworks like Software-Defined
Networking (SDN) and 5G technologies.