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Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence.

Authors :
Maharjan P
Shrestha K
Bhatta T
Cho H
Park C
Salauddin M
Rahman MT
Rana SS
Lee S
Park JY
Source :
Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Adv Sci (Weinh)] 2021 Aug; Vol. 8 (15), pp. e2100711. Date of Electronic Publication: 2021 Jun 02.
Publication Year :
2021

Abstract

Cyberattack is one of the severe threats in the digital world as it encompasses everything related to personal information, health, finances, intellectual properties, and even national security. Password-based authentication is the most practiced authentication system, however, is vulnerable to several attacks such as dictionary attack, shoulder surfing attack, and guessing attack. Here, a new keystroke dynamics-based hybrid nanogenerator for biometric authentication and identification integrated with artificial intelligence (AI) is reported. Keystroke dynamics offer behavioral and contextual information that can distinguish and authorize the individuals based on their typing rhythms. The hybrid electromagnetic-triboelectric nanogenerators/sensors efficiently convert the keystroke mechanical energy into electrical signals, which are fed into an artificial neural network based AI system. The self-powered hybrid sensors-based biometric authentication system integrated with a neural network achieves an accuracy of 99% and offers a promising hybrid security layer against password vulnerability.<br /> (© 2021 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Details

Language :
English
ISSN :
2198-3844
Volume :
8
Issue :
15
Database :
MEDLINE
Journal :
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Publication Type :
Academic Journal
Accession number :
34075718
Full Text :
https://doi.org/10.1002/advs.202100711