Know your Big Data Trade-offs when Classifying Encrypted Mobile Traffic with Deep Learning
Giuseppe Aceto, Domenico Ciuonzo, Antonio Montieri, Valerio Persico, Antonio Pescapè · 2019
The spread of handheld devices has led to the unprecedented growth of traffic volumes traversing both local networks and the Internet, appointing mobile traffic classification as a key tool for gathering highly-valuable profiling information, other than traffic engineering and service management. However, the nature of mobile traffic severely challenges state-of-art Machine-Learning (ML) approaches, since the quickly evolving and expanding set of apps generating traffic hinders ML-based approaches, that require domain-expert design. Deep Learning (DL) represents a promising solution to this issue, but results in higher completion times, in turn suggesting the application of the Big-Data (BD) paradigm. In this paper, we investigate for the first time BD-enabled classification of encrypted mobile traffic using DL from a general standpoint, (a) defining general design guidelines, (b) leveraging a public-cloud platform, and (c) resorting to a realistic experimental setup. We found that, while BD represents a transparent accelerator for some tasks, this is not the case for the training phase of DL architectures for traffic classification, requiring a specific BD-informed design. The experimental setup is built upon a three-dimensional investigation path in the BD adoption, namely: (i) completion time, (ii) deployment costs, and (iii) classification performance, highlighting relevant non-trivial trade-offs.