Abstract
In this paper, we present a behavioral biometric-based smartphone user authentication mechanism, namely, AnswerAuth, which relies on the very common users’ behavior. Behavior, here, refers to the way a user slides the lock button on the screen, to unlock the phone, and brings the phone towards her ear. The authentication mechanism works with the biometric behavior based on the extracted features from the data recorded using the built-in smartphone sensors, i.e., accelerometer, gyroscope, gravity, magnetometer and touchscreen, while the user performed sliding and phone-lifting actions. We tested AnswerAuth on a dataset of 10,200 behavioral patterns collected from 85 users while they performed the unlocking actions, in sitting, standing, and walking postures, using six state-of-the-art conceptually different machine learning classifiers in two settings, i.e., with and without simultaneous feature selection and classification. Among all the chosen classifiers, Random Forest (RF) classifier proved to be the most consistent and accurate classifier on both full and reduced features and provided a True Acceptance Rate (TAR) as high as 99.35%. We prototype proof-of-the-concept Android app, based on our findings, and evaluate it in terms of security and usability. Security analysis of AnswerAuth confirms its robustness against the possible mimicry attacks. Similarly, the usability study based on Software Usability Scale (SUS) questionnaire verifies the user-friendliness of the proposed scheme (SUS Score of 75.11). Experimental results prove AnswerAuth as a secure and usable authentication mechanism.