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Volume 18, Issue 6
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Introduction

This study investigates the impact of language complexity on Keystroke Dynamics (KD) and its implications for accurate KD-based user authentication system performance in smartphones.

Methods

This research meticulously analyzes keystroke patterns using 160 volunteers, including both frequently typed and infrequently typed texts. Our analysis of 12 anomaly detection algorithms reveals that a simple text-based KD system consistently outperforms its complex counterpart with superior Equal Error Rates (EERs).

Results

As a result, the Scaled Manhattan anomaly detector achieves an EER of 2.48% for simple text and an improvement over 2.98% for complex text. The incorporation of soft biometrics further enhances algorithmic performance, emphasizing strategies to build resilience into KD-based user authentication systems.

Conclusion

Throughout this study, the importance of text complexity is emphasized, and innovative pathways are introduced to strengthen KD-based user authentication paradigms.

© 2025 Bentham Science Publishers
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2025-09-02

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Efficacy of Keystroke Dynamics-based User Authentication in the Face of Language Complexity
Recent Advances in Computer Science and Communications 18, E120724231940 (2025); https://doi.org/10.2174/0126662558309578240705101526
/content/journals/rascs/10.2174/0126662558309578240705101526
/content/journals/rascs/10.2174/0126662558309578240705101526
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  • Article Type:     Research Article
Keyword(s): anomaly detection; complex text; Keystroke dynamics; scaled manhattan; simple text; soft biometric; user authentication

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