# Free-Text Keystroke Dynamics for User Authentication

arXiv: https://arxiv.org/abs/2107.07009 · HTML: https://mlsanity.com/paper/2107.07009/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)

## Built on by (most cited first, 6 of 6)

- [TypeFormer: Transformers for Mobile Keystroke Biometrics](https://mlsanity.com/paper/2212.13075/) (2022)
- [Type2Branch: Keystroke Biometrics based on a Dual-branch Architecture with Attention Mechanisms and Set2set Loss](https://mlsanity.com/paper/2405.01088/) (2024)
- [KVC-onGoing: Keystroke Verification Challenge](https://mlsanity.com/paper/2412.20530/) (2024)
- [Free-text Keystroke Authentication using Transformers: A Comparative Study of Architectures and Loss Functions](https://mlsanity.com/paper/2310.11640/) (2023)
- [Darknet Traffic Classification and Adversarial Attacks](https://mlsanity.com/paper/2206.06371/) (2022)
- [Keystroke Dynamics for User Identification](https://mlsanity.com/paper/2307.05529/) (2023)

## 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.