# AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification

arXiv: https://arxiv.org/abs/2308.02182 · HTML: https://mlsanity.com/paper/2308.02182/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 60 references that are in the map.
- First posted on arXiv 4 Aug 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Large-Scale Evolution of Image Classifiers](https://mlsanity.com/paper/1703.01041/) (2017)
- [Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning](https://mlsanity.com/paper/1709.02656/) (2017)
- [ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification](https://mlsanity.com/paper/2202.06335/) (2022)
- [New Directions in Automated Traffic Analysis](https://mlsanity.com/paper/2008.02695/) (2020)
- [How to Achieve High Classification Accuracy with Just a Few Labels: A Semi-supervised Approach Using Sampled Packets](https://mlsanity.com/paper/1812.09761/) (2018)
- [Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search](https://mlsanity.com/paper/1805.07440/) (2018)
- [Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications](https://mlsanity.com/paper/2104.03182/) (2021)
- [Large-scale Mobile App Identification Using Deep Learning](https://mlsanity.com/paper/1910.02350/) (2019)

## Built on by (most cited first, 2 of 2)

- [Hardware-Aware Neural Architecture Search for Encrypted Traffic Classification on Resource-Constrained Devices](https://mlsanity.com/paper/2506.11319/) (2025)
- [RepTC: Representation-Aware Optimization for Efficient Traffic Classification on Edge IoT Devices](https://mlsanity.com/paper/2610.04784/) (2026)

## Abstract (arXiv)

Deep learning (DL) has been successfully applied to encrypted network traffic classification in experimental settings. However, in production use, it has been shown that a DL classifier's performance inevitably decays over time. Re-training the model on newer datasets has been shown to only partially improve its performance. Manually re-tuning the model architecture to meet the performance expectations on newer datasets is time-consuming and requires domain expertise. We propose AutoML4ETC, a novel tool to automatically design efficient and high-performing neural architectures for encrypted traffic classification. We define a novel, powerful search space tailored specifically for the early classification of encrypted traffic using packet header bytes. We show that with different search strategies over our search space, AutoML4ETC generates neural architectures that outperform the state-of-the-art encrypted traffic classifiers on several datasets, including public benchmark datasets and real-world TLS and QUIC traffic collected from the Orange mobile network. In addition to being more accurate, AutoML4ETC's architectures are significantly more efficient and lighter in terms of the number of parameters. Finally, we make AutoML4ETC publicly available for future research.