Traffic classification in SDN-based IoT network using two-level fused network with self-adaptive manta ray foraging

Abstract

Abstract The rapid expansion of IoT networks, combined with the flexibility of Software-Defined Networking (SDN), has significantly increased the complexity of traffic management, requiring accurate classification to ensure optimal quality of service (QoS). Existing traffic classification techniques often rely on manual feature selection, limiting adaptability and efficiency in dynamic environments. This paper presents a novel traffic classification framework for SDN-based IoT networks, introducing a Two-Level Fused Network integrated with a self-adaptive Manta Ray Foraging Optimization (SMRFO) algorithm. The framework automatically selects optimal features and fuses multi-level network insights to enhance classification accuracy. Network traffic is classified into four key categories—delay-sensitive, loss-sensitive, bandwidth-sensitive, and best-effort—tailoring QoS to meet the specific requirements of each class. The proposed model is evaluated using publicly available datasets (CIC-Darknet and ISCX-ToR), achieving superior performance with over 99% accuracy. The results demonstrate the effectiveness of the Two-Level Fused Network and SMRFO in outperforming state-of-the-art classification methods, providing a scalable solution for SDN-based IoT traffic management.

Keywords

• Traffic classification
• Software defined networking
• Manta ray foraging optimization
• Gradient neural network
• Recurrent neural network
• Spiking neural network