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

TL;DR

This paper introduces DEFT, a novel distance-based feature set for keystroke dynamics that improves user identification accuracy across multiple devices by combining key distances with flight times.

Contribution

The paper proposes a new distance-enhanced feature set for keystroke dynamics, demonstrating its effectiveness across various devices and outperforming existing methods.

Findings

Accuracy exceeding 99% on all devices

Equal error rates below 10%

Device-agnostic performance

Abstract

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.