Citation

Khoh, Wee How and Teh, Pin Shen and Zhang, Ning and Tan, Syh Yuan and Shi, Qi and Nawaz, Raheel (2019) Strengthen user authentication on mobile devices by using user’s touch dynamics pattern. Journal of Ambient Intelligence and Humanized Computing (2020), 11 (10). pp. 4019-4039. ISSN 1868-5137, 1868-5145

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Abstract

Mobile devices, particularly the touch screen mobile devices, are increasingly used to store and access private and sensitive data or services, and this has led to an increased demand for more secure and usable security services, one of which is user authentication. Currently, mobile device authentication services mainly use a knowledge-based method, e.g. a PIN-based authentication method, and, in some cases, a fingerprint-based authentication method is also supported. The knowledge-based method is vulnerable to impersonation attacks, while the fingerprint-based method can be unreliable sometimes. To overcome these limitations and to make the authentication service more secure and reliable for touch screen mobile device users, we have investigated the use of touch dynamics biometrics as a mobile device authentication solution by designing, implementing and evaluating a touch dynamics authentication method. This paper describes the design, implementation, and evaluation of this method, the acquisition of raw touch dynamics data, the use of the raw data to obtain touch dynamics features, and the training of the features to build an authentication model for user identity verification. The evaluation results show that by integrating the touch dynamics authentication method into the PIN-based authentication method, the protection levels against impersonation attacks is greatly enhanced. For example, if a PIN is compromised, the success rate of an impersonation attempt is drastically reduced from 100% (if only a 4-digit PIN is used) to 9.9% (if both the PIN and the touch dynamics are used).

Item Type:	Article
Uncontrolled Keywords:	Mobile computing
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	07 Oct 2021 07:10
Last Modified:	07 Oct 2021 07:10
URII:	http://shdl.mmu.edu.my/id/eprint/8216

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