A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classification
This work addresses the challenge of frequent model updates for mobile traffic classification, which is incremental as it builds on existing incremental learning methods.
The authors tackled the problem of expensive and slow updates for deep learning models in mobile traffic classification by exploring incremental learning techniques to add new classes without full retraining, finding that while current methods like iCarl are in early stages, they show promise for speeding up model updates.
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.