Sensor-based Continuous Authentication of Smartphones' Users Using Behavioral Biometrics: A Survey release_b2rjjapeizhmvmspmu3dytxg2y

by Mohammed Abuhamad, Ahmed Abusnaina, DaeHun Nyang, David Mohaisen

Released as a article .

2020  

Abstract

Mobile devices and technologies have become increasingly popular, offering comparable storage and computational capabilities to desktop computers allowing users to store and interact with sensitive and private information. The security and protection of such personal information are becoming more and more important since mobile devices are vulnerable to unauthorized access or theft. User authentication is a task of paramount importance that grants access to legitimate users at the point-of-entry and continuously through the usage session. This task is made possible with today's smartphones' embedded sensors that enable continuous and implicit user authentication by capturing behavioral biometrics and traits. In this paper, we survey more than 140 recent behavioral biometric-based approaches for continuous user authentication, including motion-based methods (27 studies), gait-based methods (23 studies), keystroke dynamics-based methods (20 studies), touch gesture-based methods (29 studies), voice-based methods (16 studies), and multimodal-based methods (33 studies). The survey provides an overview of the current state-of-the-art approaches for continuous user authentication using behavioral biometrics captured by smartphones' embedded sensors, including insights and open challenges for adoption, usability, and performance.
In text/plain format

Archived Files and Locations

application/pdf  493.2 kB
file_ajo7msc5uzc4hi7fhzl2odxbmy
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2020-01-23
Version   v1
Language   en ?
arXiv  2001.08578v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 95c1de07-abd1-4490-990a-99eb66fe9f27
API URL: JSON