DEFT: A new distance-based feature set for keystroke dynamics
| dc.contributor.author | Kaluarachchi, Nuwan | |
| dc.contributor.author | Kandanaarachchi, Sevvandi | |
| dc.contributor.author | Moore, Kristen | |
| dc.contributor.author | Arakala, Arathi | |
| dc.contributor.editor | Damer, Naser | |
| dc.contributor.editor | Gomez-Barrero, Marta | |
| dc.contributor.editor | Raja, Kiran | |
| dc.contributor.editor | Rathgeb, Christian | |
| dc.contributor.editor | Sequeira, Ana F. | |
| dc.contributor.editor | Todisco, Massimiliano | |
| dc.contributor.editor | Uhl, Andreas | |
| dc.date.accessioned | 2023-12-12T10:46:48Z | |
| dc.date.available | 2023-12-12T10:46:48Z | |
| dc.date.issued | 2023 | |
| dc.description.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. | en |
| dc.identifier.isbn | 978-3-88579-733-3 | |
| dc.identifier.issn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/43288 | |
| dc.language.iso | en | |
| dc.pubPlace | Bonn | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | BIOSIG 2023 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-339 | |
| dc.subject | Continuous authentication | |
| dc.subject | Biometric performance measurement | |
| dc.title | DEFT: A new distance-based feature set for keystroke dynamics | en |
| dc.type | Text/Conference Paper | |
| mci.conference.date | 20.-22. September 2023 | |
| mci.conference.location | Darmstadt | |
| mci.conference.sessiontitle | Regular Research Papers | |
| mci.reference.pages | 79-89 |
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