Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation

Finamore, Alessandro; Wang, Chao; Krolikowski, Jonatan; Navarro, Jose M; Chen, Fuxing; Rossi, Dario
IMC 2023, ACM Conference on Internet Measurement, 24-26 October 2023, Montréal, QC, Canada

Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL). Yet, the vast majority of TC literature lacks code artifacts, performance assessments across datasets and reference comparisons against Machine Learning (ML) methods. Among those works, a recent study from IMC’22 [16] is worth of attention since it adopts recent DL methodologies (namely, few-shot learning, self-supervision via contrastive learning and data augmentation) appealing for networking as they enable to learn from a few samples and transfer across datasets. The main result of [16] on the UCDAVIS19, ISCX-VPN and
ISCX-Tor datasets is that, with such DL methodologies, 100 input samples are enough to achieve very high accuracy using an input representation called “flowpic” (i.e., a per-flow 2d histograms of the packets size evolution over time). In this paper (i) we reproduce [16] on the same datasets and (ii) we replicate its most salient aspect (the importance of data augmentation) on three additional public datasets (MIRAGE-19, MIRAGE-22 and UTMOBILENET21). While we confirm most of the original results,
we also found a ≈20% accuracy drop on some of the investigated scenarios due to a data shift in the original dataset that we uncovered. Additionally, our study validates that the data augmentation strategies studied in [16] perform well on other datasets
too. In the spirit of reproducibility and replicability we make all artifacts (code and data) available to the research community at https://tcbenchstack.github.io/tcbench/.

DOI
Type:
Conférence
City:
Montréal
Date:
2023-10-24
Department:
Data Science
Eurecom Ref:
7586
Copyright:
© ACM, 2023. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in IMC 2023, ACM Conference on Internet Measurement, 24-26 October 2023, Montréal, QC, Canada https://doi.org/10.1145/3618257.3624820

PERMALINK : https://www.eurecom.fr/publication/7586