# Hardware-Aware Neural Architecture Search for Encrypted Traffic Classification on Resource-Constrained Devices

arXiv: https://arxiv.org/abs/2506.11319 · HTML: https://mlsanity.com/paper/2506.11319/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 60 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://mlsanity.com/paper/1812.00332/) (2018)
- [MCUNet: Tiny Deep Learning on IoT Devices](https://mlsanity.com/paper/2007.10319/) (2020)
- [Neural Architecture Search: Insights from 1000 Papers](https://mlsanity.com/paper/2301.08727/) (2023)
- [Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning](https://mlsanity.com/paper/1709.02656/) (2017)
- [A Comprehensive Survey on Hardware-Aware Neural Architecture Search](https://mlsanity.com/paper/2101.09336/) (2021)
- [Low-latency Networking: Where Latency Lurks and How to Tame It](https://mlsanity.com/paper/1808.02079/) (2018)
- [FENXI: Deep-learning Traffic Analytics at the Edge](https://mlsanity.com/paper/2105.11738/) (2021)
- [DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors](https://mlsanity.com/paper/2107.09448/) (2021)
- [Tiny Neural Networks for Session-Level Traffic Classification](https://mlsanity.com/paper/2504.04008/) (2025)
- [An Efficient Internet Traffic Classification System Using Deep Learning for IoT](https://mlsanity.com/paper/2107.12193/) (2021)
- [AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification](https://mlsanity.com/paper/2308.02182/) (2023)
- [Searching Neural Architectures for Sensor Nodes on IoT Gateways](https://mlsanity.com/paper/2505.23939/) (2025)
- [5G/6G-Enabled Metaverse Technologies: Taxonomy, Applications, and Open Security Challenges with Future Research Directions](https://mlsanity.com/paper/2305.16473/) (2023)

## Built on by (most cited first, 3 of 3)

- [Intrusion Detection on Resource-Constrained IoT Devices with Hardware-Aware ML and DL](https://mlsanity.com/paper/2512.02272/) (2025)
- [Adversarial Robustness of Traffic Classification under Resource Constraints: Input Structure Matters](https://mlsanity.com/paper/2512.02276/) (2025)
- [RepTC: Representation-Aware Optimization for Efficient Traffic Classification on Edge IoT Devices](https://mlsanity.com/paper/2610.04784/) (2026)

## Abstract (arXiv)

This paper presents a hardware-efficient deep neural network (DNN), optimized through hardware-aware neural architecture search (HW-NAS); the DNN supports the classification of session-level encrypted traffic on resource-constrained Internet of Things (IoT) and edge devices. Thanks to HW-NAS, a 1D convolutional neural network (CNN) is tailored on the ISCX VPN-nonVPN dataset to meet strict memory and computational limits while achieving robust performance. The optimized model attains 96.60% accuracy with just 88.26K parameters, 10.08M FLOPs, and a maximum tensor size of 20.12K. Compared to state-of-the-art models, it achieves reductions of up to 444-fold, 312-fold, and 15-fold in these metrics, respectively, minimizing memory footprint and runtime requirements. The model also achieves up to 99.86% across multiple VPN and traffic classification (TC) tasks; it further generalizes to external benchmarks with up to 99.98% accuracy on USTC-TFC and QUIC NetFlow. In addition, an in-depth study of header-level preprocessing confirms that the optimized model can provide performance across a wide range of configurations, even in scenarios with stricter privacy considerations. Likewise, a reduction in the length of sessions of up to 75% yields significant improvements in efficiency, while maintaining high accuracy with only a negligible drop of 1-2%. However, the importance of careful preprocessing and session length selection in the classification of raw traffic data is still present, as improper settings or aggressive reductions can cause a 7% reduction in accuracy. The quantized architecture was deployed on STM32 microcontrollers and evaluated across input sizes; results confirm that the efficiency gains from shorter sessions translate to practical, low-latency embedded inference. These findings demonstrate the method's practicality for encrypted traffic analysis in constrained IoT networks.