Many studies have shown that single entry-point authentication schemes for smartphones can easily be circumvented. IDEAUTH is an implicit deauthentication scheme that aims to minimize unauthorized access to security-sensitive applications and services running on users’ smartphones when unauthorized access or intrusions are detected. IDEAUTH verifies legitimate owners of their smartphones by exploiting their micro hand-movements and decides to sign off the default user account revoking security-sensitive applications and services linked with it. We design and develop an Android-based prototype application as a proof-of-concept and collect a new dataset consisting of 21263 observations from 41 users in a real scenario. The user verification process employs four different one-class classifiers (OCCs), which is evaluated on the collected dataset using the holdout test method. IDEAUTH achieves a Half Total Error Rate (HTER) of ≈4% after applying a decision-level-fusion enhancing the best individual classifier's performance by ≈1%.

IDEAUTH: A novel behavioral biometric-based implicit deauthentication scheme for smartphones / Gupta, S.; Kumar, R.; Kacimi, M.; Crispo, B.. - In: PATTERN RECOGNITION LETTERS. - ISSN 0167-8655. - 157:(2022), pp. 8-15. [10.1016/j.patrec.2022.03.011]

IDEAUTH: A novel behavioral biometric-based implicit deauthentication scheme for smartphones

Gupta S.;Kumar R.;Crispo B.
2022

Abstract

Many studies have shown that single entry-point authentication schemes for smartphones can easily be circumvented. IDEAUTH is an implicit deauthentication scheme that aims to minimize unauthorized access to security-sensitive applications and services running on users’ smartphones when unauthorized access or intrusions are detected. IDEAUTH verifies legitimate owners of their smartphones by exploiting their micro hand-movements and decides to sign off the default user account revoking security-sensitive applications and services linked with it. We design and develop an Android-based prototype application as a proof-of-concept and collect a new dataset consisting of 21263 observations from 41 users in a real scenario. The user verification process employs four different one-class classifiers (OCCs), which is evaluated on the collected dataset using the holdout test method. IDEAUTH achieves a Half Total Error Rate (HTER) of ≈4% after applying a decision-level-fusion enhancing the best individual classifier's performance by ≈1%.
Gupta, S.; Kumar, R.; Kacimi, M.; Crispo, B.
IDEAUTH: A novel behavioral biometric-based implicit deauthentication scheme for smartphones / Gupta, S.; Kumar, R.; Kacimi, M.; Crispo, B.. - In: PATTERN RECOGNITION LETTERS. - ISSN 0167-8655. - 157:(2022), pp. 8-15. [10.1016/j.patrec.2022.03.011]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/339038
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