Biometric secret path for mobile user authentication: A preliminary study

Beton, Michael; Marie, Vincent; Rosenberger, Christophe

doi:10.1109/wccit.2013.6618670

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…Related work addressed verifying user identity with diverse touch measures: Shape-based phone unlock systems were enhanced with an implicit layer using touch sequence matching [1,7,18]. Characteristics of touch strokes from zooming and scrolling were utilised as well [23,58].…”

Section: Touch-based Implicit Authentication and Identificationmentioning

confidence: 99%

Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices

Buschek¹,

Luca²,

Alt³

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

104

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Authentication methods can be improved by considering implicit, individual behavioural cues. In particular, verifying users based on typing behaviour has been widely studied with physical keyboards. On mobile touchscreens, the same concepts have been applied with little adaptations so far. This paper presents the first reported study on mobile keystroke biometrics which compares touch-specific features between three different hand postures and evaluation schemes. Based on 20.160 password entries from a study with 28 participants over two weeks, we show that including spatial touch features reduces implicit authentication equal error rates (EER) by 26.4 -36.8% relative to the previously used temporal features. We also show that authentication works better for some hand postures than others. To improve applicability and usability, we further quantify the influence of common evaluation assumptions: known attacker data, training and testing on data from a single typing session, and fixed hand postures. We show that these practices can lead to overly optimistic evaluations. In consequence, we describe evaluation recommendations, a probabilistic framework to handle unknown hand postures, and ideas for further improvements.

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Section: Touch-based Implicit Authentication and Identificationmentioning

confidence: 99%

Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices

Buschek¹,

Luca²,

Alt³

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

104

View full text Add to dashboard Cite

show abstract

“…Selection of 12 subjects is backed by a research conducted by Miluzzo et al [20] who conducted their research using 10 subjects and were able to obtain a total of 40,000 tap samples for their analysis having extracted both accelerometer and gyroscope data. Other researchers who also extracted a lot of features from few individuals were Beton et al [21] who were able to obtain 120 intraclass authentication attempts and 1470 interclass attempts (simulating attacks) from 15 subjects. Because of the depth of this research, it was concluded that the 12 subjects selected were going to provide enough data.…”

Section: Methodsmentioning

confidence: 99%

“…Consequently, a warp distance of zero means that the subsequent input and reference set are identical. Closer to our study, DTW [22] was used by [16,21] to analyse their features.…”

Section: Methodsmentioning

confidence: 99%

Enhancing Accuracy in a Touch Operation Biometric System: A Case on the Android Pattern Lock Scheme

Ng’ang’a

Musuva

2020

Mobile Information Systems

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The main objective of this research study is to enhance the functionality of an Android pattern lock application by determining whether the time elements of a touch operation, in particular time on dot (TOD) and time between dot (TBD), can be accurately used as a biometric identifier. The three hypotheses that were tested through this study were the following–H1: there is a correlation between the number of touch stroke features used and the accuracy of the touch operation biometric system; H2: there is a correlation between pattern complexity and accuracy of the touch operation biometric system; H3: there is a correlation between user training and accuracy of the touch operation biometric system. Convenience sampling and a within-subjects design involving repeated measures were incorporated when testing an overall sample size of 12 subjects drawn from a university population who gave a total of 2,096 feature extracted data. Analysis was done using the Dynamic Time Warping (DTW) Algorithm. Through this study, it was shown that the extraction of one-touch stroke biometric feature coupled with user training was able to yield high average accuracy levels of up to 82%. This helps build a case for the introduction of biometrics into smart devices with average processing capabilities as they would be able to handle a biometric system without it compromising on the overall system performance. For future work, it is recommended that more work be done by applying other classification algorithms to the existing data set and comparing their results with those obtained with DTW.

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“…They obtained a performance of 19% for the FRR value (False Rejection Rate) and 21% for the FAR (False Acceptance Rate). A method in 2013 has been proposed [21] combining multiple information compared with the Pearson Correlation and the Dynamic Time warping (DTW) methods. The equal error rate (EER) is near 17% which the best result for this biometric modality.…”

Section: Biometric Pattern Based Authenticationmentioning

confidence: 99%

Synchronous One Time Biometrics with Pattern Based Authentication

Lacharme

Rosenberger

2016

2016 11th International Conference on Availability, Reliability and Security (ARES)

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Abstract-One time passwords are commonly used for authentication purposes in electronic transactions. Nevertheless, providing such a one time password is not really a strong authentication proof because the token generating the passwords can be given by an impostor. In order to cope with this problem, biometric recognition is more and more employed. Even if biometric data are strongly linked with the user, their revocability nor diversity is possible, without an adapted postprocessing. Biometric template protection schemes, including the BioHashing algorithm, are used to manage the underlying privacy and security issues. These schemes are used for the protection of several biometric modalities, but are not necessary adapted for all of them. In this paper, we propose a new protocol combining protected biometric data and a classical synchronous one time password to enhance the security of user authentication while preserving usability and privacy. Behavioral biometrics is used to provide a fast and a usable solution for users. We show through experimental results the efficiency of the proposed method.

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Biometric secret path for mobile user authentication: A preliminary study

Cited by 11 publications

References 15 publications

Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices

Improving Accuracy, Applicability and Usability of Keystroke Biometrics on Mobile Touchscreen Devices

Enhancing Accuracy in a Touch Operation Biometric System: A Case on the Android Pattern Lock Scheme

Synchronous One Time Biometrics with Pattern Based Authentication

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