Research articles featuring tcbench

Replication: Contrastive Learning and Data Augmentation in Traffic Classification
A. Finamore, C. Wang, J. Krolikowki, J. M. Navarro, F. Cheng, D. Rossi,
ACM Internet Measurement Conference (IMC), 2023
Artifacts PDF

BibtexAbstract

@misc{finamore2023contrastive,
  title={
    Contrastive Learning and Data Augmentation 
    in Traffic Classification Using a 
    Flowpic Input Representation
  }, 
  author={
    Alessandro Finamore and 
    Chao Wang and 
    Jonatan Krolikowski 
    and Jose M. Navarro 
    and Fuxing Chen and 
    Dario Rossi
  },
  year={2023},
  eprint={2309.09733},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

Over the last years we witnessed a renewed interest towards 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) meth- ods. Among those works, a recent study from IMC'22 [17] is worth of attention since it adopts recent DL methodologies (namely, few-shot learning, self-sup ervision 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 [17] 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 rep roduce [17] on the same datasets and (ii) we rep licate its most salient aspect (the importance of data augmentation) on three additional public datasets, MIRAGE-19, MIRAGE-22 and UTMOBILENET21. While we con- firm most of the original results, we also found a 20% ac- curacy drop on some of the investigated scenarios due to a data shift of the original dataset that we uncovered. Ad- ditionally, our study validates that the data augmentation strategies studied in [17] perform well on other datasets too. In the spirit of reproducibility and replicability we make all artifacts (code and data) available at [10].