  The recent popularity growth of Deep Learning (DL) re-ignited the interest
towards traffic classification, with several studies demonstrating the accuracy
of DL-based classifiers to identify Internet applications' traffic. Even with
the aid of hardware accelerators (GPUs, TPUs), DL model training remains
expensive, and limits the ability to operate frequent model updates necessary
to fit to the ever evolving nature of Internet traffic, and mobile traffic in
particular. To address this pain point, in this work we explore Incremental
Learning (IL) techniques to add new classes to models without a full
retraining, hence speeding up model's updates cycle. We consider iCarl, a state
of the art IL method, and MIRAGE-2019, a public dataset with traffic from 40
Android apps, aiming to understand "if there is a case for incremental learning
in traffic classification". By dissecting iCarl internals, we discuss ways to
improve its design, contributing a revised version, namely iCarl+. Despite our
analysis reveals their infancy, IL techniques are a promising research area on
the roadmap towards automated DL-based traffic analysis systems.
