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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