Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance. Conversely, DA has not been yet popularized in networking use cases, including Traffic Classification (TC). In this work, we present a preliminary study of 14 hand-crafted DAs applied on the MIRAGE19 dataset. Our results (i) show that DA can reap benefits previously unexplored in TC and (ii) foster a research agenda on the use of generative models to automate DA design.
@article{arxiv.2310.13935,
title = {Toward Generative Data Augmentation for Traffic Classification},
author = {Chao Wang and Alessandro Finamore and Pietro Michiardi and Massimo Gallo and Dario Rossi},
journal= {arXiv preprint arXiv:2310.13935},
year = {2023}
}