mlsanityData to 7 Oct 2026

Free-Text Keystroke Dynamics for User Authentication

arXiv 2107.07009 ↗First posted 1 Jul 2021cs.LGcited by 6

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Abstract (arXiv)

In this research, we consider the problem of verifying user identity based on keystroke dynamics obtained from free-text. We employ a novel feature engineering method that generates image-like transition matrices. For this image-like feature, a convolution neural network (CNN) with cutout achieves the best results. A hybrid model consisting of a CNN and a recurrent neural network (RNN) is also shown to outperform previous research in this field.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 3 of its 21 references that are in the map. First posted on arXiv 1 Jul 2021.

Builds on

3 of 21 references.

References · 3

  1. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  2. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
  3. 2014Deep Learning in Neural Networks: An Overviewcited by 765

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Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 2.

6 papers.

  1. 2022TypeFormer: Transformers for Mobile Keystroke Biometricscited by 7
  2. 2024Type2Branch: Keystroke Biometrics based on a Dual-branch Architecture with Attention Mechanisms and Set2set Losscited by 5
  3. 2024KVC-onGoing: Keystroke Verification Challengecited by 3
  4. 2023Free-text Keystroke Authentication using Transformers: A Comparative Study of Architectures and Loss Functionscited by 1
  5. 2022Darknet Traffic Classification and Adversarial Attackscited by 0
  6. 2023Keystroke Dynamics for User Identificationcited by 0