Cargando…
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...
Autores principales: | , , , , , , , , , |
---|---|
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 |
_version_ | 1783733332516798464 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8336502 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT maharjanpukar keystrokedynamicsbasedhybridnanogeneratorsforbiometricauthenticationandidentificationusingartificialintelligence AT shresthakumar keystrokedynamicsbasedhybridnanogeneratorsforbiometricauthenticationandidentificationusingartificialintelligence AT bhattatrilochan keystrokedynamicsbasedhybridnanogeneratorsforbiometricauthenticationandidentificationusingartificialintelligence AT chohyunok keystrokedynamicsbasedhybridnanogeneratorsforbiometricauthenticationandidentificationusingartificialintelligence AT parkchani keystrokedynamicsbasedhybridnanogeneratorsforbiometricauthenticationandidentificationusingartificialintelligence AT salauddinmd keystrokedynamicsbasedhybridnanogeneratorsforbiometricauthenticationandidentificationusingartificialintelligence AT rahmanmtoyabur keystrokedynamicsbasedhybridnanogeneratorsforbiometricauthenticationandidentificationusingartificialintelligence AT ranasmsohel keystrokedynamicsbasedhybridnanogeneratorsforbiometricauthenticationandidentificationusingartificialintelligence AT leesanghyun keystrokedynamicsbasedhybridnanogeneratorsforbiometricauthenticationandidentificationusingartificialintelligence AT parkjaey keystrokedynamicsbasedhybridnanogeneratorsforbiometricauthenticationandidentificationusingartificialintelligence |