mlsanityData to 14 Oct 2026

Data Augmentation for Traffic Classification

arXiv 2401.10754 ↗First posted 19 Jan 2024cs.LG cs.NIcited by 3

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Abstract (arXiv)

Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks to improve models performance. Yet, DA has struggled to gain traction in networking contexts, particularly in Traffic Classification (TC) tasks. In this work, we fulfill this gap by benchmarking 18 augmentation functions applied to 3 TC datasets using packet time series as input representation and considering a variety of training conditions. Our results show that (i) DA can reap benefits previously unexplored, (ii) augmentations acting on time series sequence order and masking are better suited for TC than amplitude augmentations and (iii) basic models latent space analysis can help understanding the positive/negative effects of augmentations on classification performance.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 26 of its 58 references that are in the map. First posted on arXiv 19 Jan 2024.

Builds on

26 of 58 references.

References · 26

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14 more
  1. 2023Effective Data Augmentation With Diffusion Modelscited by 283
  2. 2021TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentationcited by 227
  3. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 226
  4. 2019Implicit Semantic Data Augmentation for Deep Networkscited by 106
  5. 2023DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networkscited by 20
  6. 2018How to Achieve High Classification Accuracy with Just a Few Labels: A Semi-supervised Approach Using Sampled Packetscited by 19
  7. 2023The Benefits of Mixup for Feature Learningcited by 19
  8. 2023NetDiffus: Network Traffic Generation by Diffusion Models through Time-Series Imagingcited by 19
  9. 2022Fine-grained TLS services classification with reject optioncited by 14
  10. 2019PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGANcited by 11
  11. 2022Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wildcited by 8
  12. 2021A First Look at Class Incremental Learning in Deep Learning Mobile Traffic Classificationcited by 4
  13. 2023Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classificationcited by 4
  14. 2022Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025When Simple Model Just Works: Is Network Traffic Classification in Crisis?cited by 4
  2. 2025Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Successcited by 1
  3. 2026Lightweight GenAI for Network Traffic Generation: Fidelity, Augmentation, and Classificationcited by 0