# DEFT: A new distance-based feature set for keystroke dynamics

arXiv: https://arxiv.org/abs/2310.04059 · HTML: https://mlsanity.com/paper/2310.04059/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 19 references that are in the map.
- First posted on arXiv 6 Oct 2023.

## Builds on (most cited first, 2 of 2)

- [TypeNet: Deep Learning Keystroke Biometrics](https://mlsanity.com/paper/2101.05570/) (2021)
- [Insights from BB-MAS -- A Large Dataset for Typing, Gait and Swipes of the Same Person on Desktop, Tablet and Phone](https://mlsanity.com/paper/1912.02736/) (2019)

## Built on by (most cited first, 1 of 1)

- [Transfer Learning of Keystroke Dynamics for Cross-Device User Authentication](https://mlsanity.com/paper/2608.16334/) (2026)

## Abstract (arXiv)

Keystroke dynamics is a behavioural biometric utilised for user identification and authentication. We propose a new set of features based on the distance between keys on the keyboard, a concept that has not been considered before in keystroke dynamics. We combine flight times, a popular metric, with the distance between keys on the keyboard and call them as Distance Enhanced Flight Time features (DEFT). This novel approach provides comprehensive insights into a person's typing behaviour, surpassing typing velocity alone. We build a DEFT model by combining DEFT features with other previously used keystroke dynamic features. The DEFT model is designed to be device-agnostic, allowing us to evaluate its effectiveness across three commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms the existing state-of-the-art methods when we evaluate its effectiveness across two datasets. We obtain accuracy rates exceeding 99% and equal error rates below 10% on all three devices.