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This paper presents a system for session-level traffic classification on endpoint devices, developed using a Hardware-aware Neural Architecture Search (HW-NAS) framework. HW-NAS optimizes Convolutional Neural Network (CNN) architectures by integrating hardware constraints, ensuring efficient deployment on resource-constrained devices. Tested on the ISCX VPN-nonVPN dataset, the method achieves 97.06% accuracy while reducing parameters by over 200 times and FLOPs by nearly 4 times compared to leading models. The proposed model requires up to 15.5 times less RAM and 26.4 times fewer FLOPs than the most hardware-demanding models. This system enhances compatibility across network architectures and ensures efficient deployment on diverse hardware, making it suitable for applications like firewall policy enforcement and traffic monitoring.

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Tiny Neural Networks for Session-Level Traffic Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Adel Chehade,
  • Edoardo Ragusa,
  • Paolo Gastaldo,
  • Rodolfo Zunino

摘要

This paper presents a system for session-level traffic classification on endpoint devices, developed using a Hardware-aware Neural Architecture Search (HW-NAS) framework. HW-NAS optimizes Convolutional Neural Network (CNN) architectures by integrating hardware constraints, ensuring efficient deployment on resource-constrained devices. Tested on the ISCX VPN-nonVPN dataset, the method achieves 97.06% accuracy while reducing parameters by over 200 times and FLOPs by nearly 4 times compared to leading models. The proposed model requires up to 15.5 times less RAM and 26.4 times fewer FLOPs than the most hardware-demanding models. This system enhances compatibility across network architectures and ensures efficient deployment on diverse hardware, making it suitable for applications like firewall policy enforcement and traffic monitoring.