mlsanityData to 14 Oct 2026

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers

arXiv 2506.10851 ↗First posted 12 Jun 2025cs.NI cs.LGcited by 4

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Abstract (arXiv)

In this paper, we present a practical deep learning (DL) approach for energy-efficient traffic classification (TC) on resource-limited microcontrollers, which are widely used in IoT-based smart systems and communication networks. Our objective is to balance accuracy, computational efficiency, and real-world deployability. To that end, we develop a lightweight 1D-CNN, optimized via hardware-aware neural architecture search (HW-NAS), which achieves 96.59% accuracy on the ISCX VPN-NonVPN dataset with only 88.26K parameters, a 20.12K maximum tensor size, and 10.08M floating-point operations (FLOPs). Moreover, it generalizes across various TC tasks, with accuracies ranging from 94% to 99%. To enable deployment, the model is quantized to INT8, suffering only a marginal 1-2% accuracy drop relative to its Float32 counterpart. We evaluate real-world inference performance on two microcontrollers: the high-performance STM32F746G-DISCO and the cost-sensitive Nucleo-F401RE. The deployed model achieves inference latencies of 31.43ms and 115.40ms, with energy consumption of 7.86 mJ and 29.10 mJ per inference, respectively. These results demonstrate the feasibility of on-device encrypted traffic analysis, paving the way for scalable, low-power IoT security solutions.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 8 of its 31 references that are in the map. First posted on arXiv 12 Jun 2025.

Builds on

8 of 31 references.

References · 8

  1. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  2. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  3. 2023Neural Architecture Search: Insights from 1000 Paperscited by 139
  4. 2017Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learningcited by 70
  5. 2021A Comprehensive Survey on Hardware-Aware Neural Architecture Searchcited by 61
  6. 2022Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Studycited by 6
  7. 2024Multi-Objective Hardware Aware Neural Architecture Search using Hardware Cost Diversitycited by 5
  8. 2025Tiny Neural Networks for Session-Level Traffic Classificationcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applicationscited by 7
  2. 2025Intrusion Detection on Resource-Constrained IoT Devices with Hardware-Aware ML and DLcited by 0
  3. 2025Adversarial Robustness of Traffic Classification under Resource Constraints: Input Structure Matterscited by 0
  4. 2026RepTC: Representation-Aware Optimization for Efficient Traffic Classification on Edge IoT Devicescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Hardware-Aware Neural Architecture Search for Encrypted Traffic Classification on Resource-Constrained Devices
  2. 2022Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression
  3. 2026NetMamba+: A Framework of Pre-trained Models for Efficient and Accurate Network Traffic Classification
  4. 2025M3-Net: A Cost-Effective Graph-Free MLP-Based Model for Traffic Prediction
  5. 2024NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mamba
  6. 2025Evaluating the Energy Efficiency of NPU-Accelerated Machine Learning Inference on Embedded Microcontrollers
  7. 2021Quantization and Deployment of Deep Neural Networks on Microcontrollers
  8. 2026PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units