# Fast Free-text Authentication via Instance-based Keystroke Dynamics

arXiv: https://arxiv.org/abs/2006.09337 · HTML: https://mlsanity.com/paper/2006.09337/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [TypeNet: Deep Learning Keystroke Biometrics](https://mlsanity.com/paper/2101.05570/) (2021)
- [TypeFormer: Transformers for Mobile Keystroke Biometrics](https://mlsanity.com/paper/2212.13075/) (2022)
- [Keystroke Dynamics Against Academic Dishonesty in the Age of LLMs](https://mlsanity.com/paper/2406.15335/) (2024)
- [BeCAPTCHA-Type: Biometric Keystroke Data Generation for Improved Bot Detection](https://mlsanity.com/paper/2207.13394/) (2022)
- [Free-text Keystroke Authentication using Transformers: A Comparative Study of Architectures and Loss Functions](https://mlsanity.com/paper/2310.11640/) (2023)
- [Machine Learning-Based Analysis of Free-Text Keystroke Dynamics](https://mlsanity.com/paper/2107.07409/) (2021)
- [A novel non-linear transformation based multi-user identification algorithm for fixed text keystroke behavioral dynamics](https://mlsanity.com/paper/2210.02505/) (2022)
- [Template Ageing and Longitudinal Verification in Fixed-Text Keystroke Dynamics: A Subject-Disjoint Study Across Eight Weeks](https://mlsanity.com/paper/2609.29851/) (2026)

## Abstract (arXiv)

Keystroke dynamics study the way in which users input text via their keyboards. Having the ability to differentiate users, typing behaviors can unobtrusively form a component of a behavioral biometric recognition system to improve existing account security. Keystroke dynamics systems on free-text data have previously required 500 or more characters to achieve reasonable performance. In this paper, we propose a novel instance-based graph comparison algorithm called the instance-based tail area density (ITAD) metric to reduce the number of keystrokes required to authenticate users. Additionally, commonly used features in the keystroke dynamics literature, such as monographs and digraphs, are all found to be useful in informing who is typing. The usefulness of these features for authentication is determined using a random forest classifier and validated across two publicly available datasets. Scores from the individual features are fused to form a single matching score. With the fused matching score and our ITAD metric, we achieve equal error rates (EERs) for 100 and 200 testing digraphs of 9.7% and 7.8% for the Clarkson II dataset, improving upon state-of-the-art of 35.3% and 15.3%.