Citation

Lim, Wun Puo and Ooi, Shih Yin and Pang, Ying Han and Ramalingam, Soodamani and Chew, Yee Jian (2024) Dynamic Touchstroke Analysis with Explainable Artificial Intelligence Tree-Based Learners. Journal of Telecommunications and the Digital Economy, 12 (4). pp. 137-161. ISSN 2203-1693

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Abstract

As mobile devices become integral to daily life, robust authentication methods are essential for ensuring security. Traditional methods like personal identification numbers and swipe patterns remain vulnerable to social engineering attacks. To address these risks, this study investigates behavioural biometrics, specifically touch-stroke dynamics, as a transparent and secure alternative. By leveraging unique user interaction patterns, such as touch speed and pressure, this approach provides a distinctive means of authentication. Although various machine learning techniques are available for touch-stroke analysis, the interpretability of classification decisions is vital. This paper implements explainable artificial intelligence with tree-based learners, specifically decision trees and random forests, to enhance the transparency and effectiveness of touch-stroke dynamic authentication. Performance evaluations show that random forests achieve equal error rates (EER) between 0.03% and 0.05%, and decision trees yield EERs between 0.02% and 0.07%, demonstrating a balance between security and interpretability for mobile authentication.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Feb 2025 00:42
Last Modified:	07 Feb 2025 00:42
URII:	http://shdl.mmu.edu.my/id/eprint/13386

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