  Data Augmentation (DA) -- enriching training data by adding synthetic samples
-- is a technique widely adopted in Computer Vision (CV) and Natural Language
Processing (NLP) tasks to improve models performance. Yet, DA has struggled to
gain traction in networking contexts, particularly in Traffic Classification
(TC) tasks. In this work, we fulfill this gap by benchmarking 18 augmentation
functions applied to 3 TC datasets using packet time series as input
representation and considering a variety of training conditions. Our results
show that (i) DA can reap benefits previously unexplored, (ii) augmentations
acting on time series sequence order and masking are better suited for TC than
amplitude augmentations and (iii) basic models latent space analysis can help
understanding the positive/negative effects of augmentations on classification
performance.
