  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.
