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AUToSen: Deep-Learning-Based Implicit Continuous Authentication Using Smartphone Sensors

Title
AUToSen: Deep-Learning-Based Implicit Continuous Authentication Using Smartphone Sensors
Authors
Abuhamad, MohammedAbuhmed, TamerMohaisen, DavidNyang, DaeHun
Ewha Authors
양대헌
Issue Date
2020
Journal Title
IEEE INTERNET OF THINGS JOURNAL
ISSN
2327-4662JCR Link
Citation
IEEE INTERNET OF THINGS JOURNAL vol. 7, no. 6, pp. 5008 - 5020
Keywords
AuthenticationMagnetic sensorsBiometrics (access control)MagnetometersSensor phenomena and characterizationAccelerometersActive authenticationcontinuous authenticationdeep-learning-based authenticationmobile sensingsmartphone authentication
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Indexed
SCIE; SCOPUS WOS
Document Type
Article
Abstract
Smartphones have become crucial for our daily life activities and are increasingly loaded with our personal information to perform several sensitive tasks, including, mobile banking and communication, and are used for storing private photos and files. Therefore, there is a high demand for applying usable authentication techniques that prevent unauthorized access to sensitive information. In this article, we propose AUToSen, a deep-learning-based active authentication approach that exploits sensors in consumer-grade smartphones to authenticate a user. Unlike conventional approaches, AUToSen is based on deep learning to identify user distinct behavior from the embedded sensors with and without the user's interaction with the smartphone. We investigate different deep learning architectures in modeling and capturing users' behavioral patterns for the purpose of authentication. Moreover, we explore the sufficiency of sensory data required to accurately authenticate users. We evaluate AUToSen on a real-world data set that includes sensors data of 84 participants' smartphones collected using our designed data-collection application. The experiments show that AUToSen operates accurately using readings of only three sensors (accelerometer, gyroscope, and magnetometer) with a high authentication frequency, e.g., one authentication attempt every 0.5 s. Using sensory data of one second enables an authentication F1-score of approximately 98%, false acceptance rate (FAR) of 0.95%, false rejection rate (FRR) of 6.67%, and equal error rate (EER) of 0.41%. While using sensory data of half a second enables an authentication F1-score of 97.52%, FAR of 0.96%, FRR of 8.08%, and EER of 0.09%. Moreover, we investigate the effects of using different sensory data at variable sampling periods on the performance of the authentication models under various settings and learning architectures.
DOI
10.1109/JIOT.2020.2975779
Appears in Collections:
ETC > ETC
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