Real-Time Network Traffic Classification in IoT Networks Using Hybrid AI Techniques
Farzam Rezaei, Jorge E. López de Vergara · 2025
The development of IoT across various fields emphasizes the need for real-time network traffic classification to maintain security, simplify resources, and address evolving threats. Traditional methods like port-based and deep packet inspection fail with encrypted traffic, privacy constraints, and slow processing, while deep learning solutions face limitations with insufficient data and latency in IoT environments. To overcome these challenges, this research presents a hybrid AI framework that seamlessly combines AI techniques for fast and accurate classification. Through a five-phase process-data preparation, feature engineering, model design, interpretability and optimization, and deployment and validation-it merges synthetic traffic generation and semi-supervised methods to improve data scarcity. Optimized deep neural networks and GAN networks will be used for classification and anomaly detection. Moreover, the results will be enhanced by applying explainable artificial intelligence for transparency. This framework aims to improve latency and accuracy, outperforming current approaches.