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arXiv · 2310.13935

Toward Generative Data Augmentation for Traffic Classification

Chao Wang·Alessandro Finamore·Pietro Michiardi·Massimo Gallo·Dario Rossi
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Abstract

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.

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Chao Wang, Alessandro Finamore, Pietro Michiardi, Massimo Gallo, Dario Rossi. 2023-10-21. Toward Generative Data Augmentation for Traffic Classification. https://arxiv.org/abs/2310.13935

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