# Tiny Neural Networks for Session-Level Traffic Classification

arXiv: https://arxiv.org/abs/2504.04008 · HTML: https://mlsanity.com/paper/2504.04008/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2025.

## Built on by (most cited first, 5 of 5)

- [Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers](https://mlsanity.com/paper/2506.10851/) (2025)
- [Hardware-Aware Neural Architecture Search for Encrypted Traffic Classification on Resource-Constrained Devices](https://mlsanity.com/paper/2506.11319/) (2025)
- [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 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.