AUToSen: Deep-learning-based implicit continuous authentication using smartphone sensors

Mohammed Abuhamad, Tamer Abuhmed, David Mohaisen, Daehun Nyang

Research output: Contribution to journalArticlepeer-review

91 Scopus citations


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.

Original languageEnglish
Article number9007368
Pages (from-to)5008-5020
Number of pages13
JournalIEEE Internet of Things Journal
Issue number6
StatePublished - Jun 2020

Bibliographical note

Publisher Copyright:
© 2014 IEEE.


  • Active authentication
  • Continuous authentication
  • Deep-learning-based authentication
  • Mobile sensing
  • Smartphone authentication


Dive into the research topics of 'AUToSen: Deep-learning-based implicit continuous authentication using smartphone sensors'. Together they form a unique fingerprint.

Cite this