| Title | Keystroke Dynamics based User Authentication using Deep Learning Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Piugie, Yris Brice Wandji, Di Manno, Joël, Rosenberger, Christophe, Charrier, Christophe |
| Conference Name | 2022 International Conference on Cyberworlds (CW) |
| Keywords | authentication, Behavioral biometrics, convolutional neural networks, Human Behavior, Industries, keystroke analysis, keystroke dynamics, Metrics, Neural networks, passwords, pubcrawl, security, Three-dimensional displays, Time series analysis, user authentication |
| Abstract | Keystroke dynamics is one solution to enhance the security of password authentication without adding any disruptive handling for users. Industries are looking for more security without impacting too much user experience. Considered as a friction-less solution, keystroke dynamics is a powerful solution to increase trust during user authentication without adding charge to the user. In this paper, we address the problem of user authentication considering the keystroke dynamics modality. We proposed a new approach based on the conversion of behavioral biometrics data (time series) into a 3D image. This transformation process keeps all the characteristics of the behavioral signal. The time series do not receive any filtering operation with this transformation and the method is bijective. This transformation allows us to train images based on convolutional neural networks. We evaluate the performance of the authentication system in terms of Equal Error Rate (EER) on a significant dataset and we show the efficiency of the proposed approach on a multi-instance system. |
| Notes | ISSN: 2642-3596 |
| DOI | 10.1109/CW55638.2022.00052 |
| Citation Key | piugie_keystroke_2022 |
Keystroke Dynamics based User Authentication using Deep Learning Neural Networks