User Behavioral Biometrics and Machine Learning Towards Improving User Authentication in Smartphones

2020 ◽  
pp. 250-271
Author(s):  
José Torres ◽  
Sergio de los Santos ◽  
Efthimios Alepis ◽  
Constantinos Patsakis
Keyword(s):  
Machine Learning ◽  
User Authentication ◽  
Behavioral Biometrics

Hold and Sign: A Novel Behavioral Biometrics for Smartphone User Authentication

2016 ◽  
Author(s):  
Attaullah Buriro ◽  
Bruno Crispo ◽  
Filippo Delfrari ◽  
Konrad Wrona
Keyword(s):  
User Authentication ◽  
Behavioral Biometrics

Continuous User Authentication Using Machine Learning on Touch Dynamics

2015 ◽  
pp. 591-598 ◽  
Author(s):  
Ştefania Budulan ◽  
Elena Burceanu ◽  
Traian Rebedea ◽  
Costin Chiru
Keyword(s):  
Machine Learning ◽  
User Authentication ◽  
Continuous User

scholarly journals Vulnerability Analysis Challenges of the Mouse Data Based on Machine Learning for Image-Based User Authentication

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 177241-177253 ◽  
Author(s):  
Kyungroul Lee ◽  
Christian Esposito ◽  
Sun-Young Lee
Keyword(s):  
Machine Learning ◽  
User Authentication ◽  
Vulnerability Analysis

scholarly journals A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication

2018 ◽  
Author(s):  
Nurul Afnan Mahadi ◽  
Mohamad Afendee Mohamed ◽  
Amirul Ihsan Mohamad ◽  
Mokhairi Makhtar ◽  
Mohd Fadzil Abdul Kadir ◽  
...  
Keyword(s):  
Machine Learning ◽  
User Authentication ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Keystroke dynamics Based Technique to Enhance the Security in Smart Devices

2021 ◽  
Vol 4 (1) ◽  
pp. 14
Author(s):  
Farman Pirzado ◽  
Shahzad Memon ◽  
Lachman Das Dhomeja Dhomeja ◽  
Awais Ahmed
Keyword(s):  
Machine Learning ◽  
User Authentication ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Mobile Banking ◽  
Alternative Technique ◽  
Keystroke Dynamics ◽  
Data Set ◽  
Proposed Model

Nowadays, smart devices have become a part of ourlives, hold our data, and are used for sensitive transactions likeinternet banking, mobile banking, etc. Therefore, it is crucial tosecure the data in these smart devices from theft or misplacement.The majority of the devices are secured with password/PINbaseduser authentication methods, which are already proveda less secure or easily guessable user authentication method.An alternative technique for securing smart devices is keystrokedynamics. Keystroke dynamics (KSD) is behavioral biometrics,which uses a natural typing pattern unique in every individualand difficult to fake or replicates that pattern. This paperproposes a user authentication model based on KSD as an additionalsecurity method for increasing the smart devices’ securitylevel. In order to analyze the proposed model, an android-basedapplication has been implemented for collecting data from fakeand genuine users. Six machine learning algorithms have beentested on the collected data set to study their suitability for usein the keystroke dynamics-based authentication model.

High Security User Authentication Enabled by Piezoelectric Keystroke Dynamics and Machine Learning

2020 ◽  
Vol 20 (21) ◽  
pp. 13037-13046
Author(s):  
Anbiao Huang ◽  
Shuo Gao ◽  
Junliang Chen ◽  
Lijun Xu ◽  
Arokia Nathan
Keyword(s):  
Machine Learning ◽  
User Authentication ◽  
Keystroke Dynamics ◽  
High Security
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