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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.04008 |
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| _version_ | 1866909606941294592 |
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| author | Chehade, Adel Ragusa, Edoardo Gastaldo, Paolo Zunino, Rodolfo |
| author_facet | Chehade, Adel Ragusa, Edoardo Gastaldo, Paolo Zunino, Rodolfo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04008 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tiny Neural Networks for Session-Level Traffic Classification Chehade, Adel Ragusa, Edoardo Gastaldo, Paolo Zunino, Rodolfo Networking and Internet Architecture 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. |
| title | Tiny Neural Networks for Session-Level Traffic Classification |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2504.04008 |