Open AccessKeystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence
Author(s) -
Maharjan Pukar,
Shrestha Kumar,
Bhatta Trilochan,
Cho Hyunok,
Park Chani,
Salauddin Md,
Rahman M. Toyabur,
Rana SM Sohel,
Lee Sanghyun,
Park Jae Y.
Publication year - 2021
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202100711
Subject(s) - keystroke dynamics , password , biometrics , computer science , authentication (law) , identification (biology) , computer security , artificial neural network , keystroke logging , artificial intelligence , vulnerability (computing) , human–computer interaction , s/key , biology , botany
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.