Deep Learning-Based Network Traffic Classification for Next-Generation Networks

Abstract

Network traffic classification has become essential for managing, securing, and optimizing next-generation networks (NGNs) that support IoT, cloud computing, SDN, NFV, and 5G technologies. Traditional classification methods based on port numbers and deep packet inspection are increasingly ineffective due to encrypted traffic, dynamic port allocation, and evolving application behaviors.This study presents a deep learning-based approach for intelligent network traffic classification. It reviews the evolution of traffic classification techniques and proposes a hybrid CNN-LSTM model that combines packet-level feature extraction with temporal sequence learning. The framework effectively captures spatial and temporal characteristics of network traffic, enabling accurate classification of both encrypted and non-encrypted flows. Experimental evaluation using benchmark datasets demonstrates that the proposed model outperforms conventional machine learning methods, achieving classification accuracy above 97%. Performance is validated using metrics such as accuracy, precision, recall, F1-score, and classification efficiency. The results highlight the potential of deep learning to support real-time traffic analysis, network security, QoS management, and resource optimization in modern communication networks. The proposed framework provides an intelligent and scalable solution for traffic classification in future autonomous and secure networking environments.