Toward Generative Data Augmentation for Traffic Classification
Chao Wang Alessandro Finamore Pietro Michiardi Massimo Gallo Dario Rossi
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.