  The popularity of Deep Learning (DL), coupled with network traffic visibility
reduction due to the increased adoption of HTTPS, QUIC and DNS-SEC, re-ignited
interest towards Traffic Classification (TC). However, to tame the dependency
from task-specific large labeled datasets we need to find better ways to learn
representations that are valid across tasks. In this work we investigate this
problem comparing transfer learning, meta-learning and contrastive learning
against reference Machine Learning (ML) tree-based and monolithic DL models (16
methods total). Using two publicly available datasets, namely MIRAGE19 (40
classes) and AppClassNet (500 classes), we show that (i) using large datasets
we can obtain more general representations, (ii) contrastive learning is the
best methodology and (iii) meta-learning the worst one, and (iv) while ML
tree-based cannot handle large tasks but fits well small tasks, by means of
reusing learned representations, DL methods are reaching tree-based models
performance also for small tasks.
