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.
翻译:最近深层学习(DL)的普及增长重新点燃了对交通分类的兴趣,有几项研究表明了基于DL的分类员识别互联网应用流量的准确性。即使有了硬件加速器(GPU、TPUs)的帮助,DL模式培训仍然费用高昂,限制了经常进行符合互联网交通、特别是移动交通不断变化的性质所需的模型更新的能力。为了解决这一痛苦点,我们在这项工作中探索了递增学习(IL)技术,以便在没有全面再培训的情况下将新课程添加到模型中,从而加快模型更新周期。我们认为iCarl是IL方法的一种状态,而MIRAGE-2019是一个公共数据集,有40个和机器人的流量,旨在理解“如果在交通分类方面有逐步学习的理由的话 ” 。 我们通过解开iCarl内部, 讨论如何改进其设计的方法, 即iCarl+ 。 尽管我们的分析揭示了他们的幼年期, IL技术是通往自动使用DL交通分析系统路线图的一个很有希望的研究领域。