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

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 seve...

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Autores principales: Maharjan, Pukar, Shrestha, Kumar, Bhatta, Trilochan, Cho, Hyunok, Park, Chani, Salauddin, Md, Rahman, M. Toyabur, Rana, SM Sohel, Lee, Sanghyun, Park, Jae Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336502/
https://www.ncbi.nlm.nih.gov/pubmed/34075718
http://dx.doi.org/10.1002/advs.202100711
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author Maharjan, Pukar
Shrestha, Kumar
Bhatta, Trilochan
Cho, Hyunok
Park, Chani
Salauddin, Md
Rahman, M. Toyabur
Rana, SM Sohel
Lee, Sanghyun
Park, Jae Y.
author_facet Maharjan, Pukar
Shrestha, Kumar
Bhatta, Trilochan
Cho, Hyunok
Park, Chani
Salauddin, Md
Rahman, M. Toyabur
Rana, SM Sohel
Lee, Sanghyun
Park, Jae Y.
author_sort Maharjan, Pukar
collection PubMed
description 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.
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spelling pubmed-83365022021-08-09 Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence Maharjan, Pukar Shrestha, Kumar Bhatta, Trilochan Cho, Hyunok Park, Chani Salauddin, Md Rahman, M. Toyabur Rana, SM Sohel Lee, Sanghyun Park, Jae Y. Adv Sci (Weinh) Research Articles 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. John Wiley and Sons Inc. 2021-06-02 /pmc/articles/PMC8336502/ /pubmed/34075718 http://dx.doi.org/10.1002/advs.202100711 Text en © 2021 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Maharjan, Pukar
Shrestha, Kumar
Bhatta, Trilochan
Cho, Hyunok
Park, Chani
Salauddin, Md
Rahman, M. Toyabur
Rana, SM Sohel
Lee, Sanghyun
Park, Jae Y.
Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence
title Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence
title_full Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence
title_fullStr Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence
title_full_unstemmed Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence
title_short Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence
title_sort keystroke dynamics based hybrid nanogenerators for biometric authentication and identification using artificial intelligence
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336502/
https://www.ncbi.nlm.nih.gov/pubmed/34075718
http://dx.doi.org/10.1002/advs.202100711
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