AutoML4ETC: Automated Neural Architecture Search for Real-World Encrypted Traffic Classification. (arXiv:2308.02182v1 [cs.NI])

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 …

architecture automated classification datasets deep learning encrypted encrypted traffic network network traffic performance search settings traffic traffic classification training world