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

• URL: http://arxiv.org/abs/2310.04059v1
• Date: Fri, 6 Oct 2023 07:26:40 GMT
• Title: DEFT: A new distance-based feature set for keystroke dynamics
• Authors: Nuwan Kaluarachchi, Sevvandi Kandanaarachchi, Kristen Moore and Arathi Arakala
• Abstract summary: 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 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.
• Score: 1.8796659304823702
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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