Free-Text Keystroke Dynamics for User Authentication
- URL: http://arxiv.org/abs/2107.07009v1
- Date: Thu, 1 Jul 2021 14:46:10 GMT
- Title: Free-Text Keystroke Dynamics for User Authentication
- Authors: Jianwei Li, Han-Chih Chang, Mark Stamp
- Abstract summary: We consider the problem of verifying user identity based on keystroke dynamics obtained from free-text.
For this image-like feature, a convolution neural network (CNN) with cutout achieves the best results.
A hybrid model consisting of a CNN and a recurrent neural network (RNN) is also shown to outperform previous research in this field.
- Score: 7.447152998809457
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this research, we consider the problem of verifying user identity based on
keystroke dynamics obtained from free-text. We employ a novel feature
engineering method that generates image-like transition matrices. For this
image-like feature, a convolution neural network (CNN) with cutout achieves the
best results. A hybrid model consisting of a CNN and a recurrent neural network
(RNN) is also shown to outperform previous research in this field.
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