Traffic Classification in SDN-Based IoT Network using Two-Level Fused Network with Self-Adaptive Manta Ray Foraging DOI Creative Commons
Mohammed A. H. Ali

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 8, 2024

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

Language: Английский

Traffic Classification in SDN-Based IoT Network using Two-Level Fused Network with Self-Adaptive Manta Ray Foraging DOI Creative Commons
Mohammed A. H. Ali

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 8, 2024

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

Language: Английский

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