In plain words: It pairs the time between two key presses with the distance between those keys to tell people apart by how they type, capturing rhythm better than speed alone. Tested on desktop, mobile, and tablet, it beat the best earlier methods with over 99% accuracy.
Abstract · DEFT: A new distance-based feature set for keystroke dynamics
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.
Nuwan Kaluarachchi, Sevvandi Kandanaarachchi, Kristen Moore, Arathi Arakala
arXiv:2310.04059 · cs.LG · submitted Oct 6, 2023
abstract · pdf · html · 12 pages, 5 figures, 3 tables, conference paper