Exploring Keystroke Dynamics: Enhancing Authentication Through Typing Patterns

DOI

10.2991/978-94-6463-858-5_261How to use a DOI?

Keywords

Keystroke Dynamics; Continuous Authentication; Biometrics

Abstract

The evolution of cybersecurity in a landscape with observable changes calls for sturdy and inventive alternatives for the escalating threats to traditional authentication approaches such as passwords and personal identification numbers (PIN). Keystroke dynamics emerges as a behavioral biometric that also utilizes the individual’s own typing pattern in terms of key press durations and intervals for secure and continuous authentication. Contrasting with hardware-heavy biometric systems, keystroke dynamics is device-independent which makes it cost-effective and practical for various applications, such as banking, smartphones, and access control. Recent advancements have demonstrated significant improvements through machine learning techniques, such as Isolation Forest, Mahalanobis distance, and wavelet transformations, which enhance accuracy by 98.4% and robustness in predicting frauds. The comparison analyses, involving evaluation of various algorithms such as SVMs, Random Forest, and Artificial Bee Colony, indicate significantly reduced values of EER up to 1.83%, affirming the reliability and efficacy of keystroke dynamics for secure authentication. Furthermore, the integration of feature extraction methods and multimodal biometric approaches has strengthened its adaptability to real-world applications. This paper reviews the recent progress, techniques, and challenges in keystroke dynamics, examining its input in modern authentication systems, highlighting future research directions for achieving enhanced security in an increasingly digital world.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Akshansh Shrivastava
AU  - Swarnalata Bollavarapu
PY  - 2025
DA  - 2025/11/04
TI  - Exploring Keystroke Dynamics: Enhancing Authentication Through Typing Patterns
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3127
EP  - 3140
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_261
DO  - 10.2991/978-94-6463-858-5_261
ID  - Shrivastava2025
ER  -