Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics

• URL: http://arxiv.org/abs/2107.00507v1
• Date: Thu, 1 Jul 2021 14:54:29 GMT
• Title: Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics
• Authors: Han-Chih Chang, Jianwei Li, Ching-Seh Wu, Mark Stamp
• Abstract summary: Keystroke dynamics can be used to analyze the way that users type by measuring various aspects of keyboard input. We consider a wide variety of machine learning and deep learning techniques based on fixed-text keystroke-derived features.
• Score: 6.626171743551614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Keystroke dynamics can be used to analyze the way that users type by measuring various aspects of keyboard input. Previous work has demonstrated the feasibility of user authentication and identification utilizing keystroke dynamics. In this research, we consider a wide variety of machine learning and deep learning techniques based on fixed-text keystroke-derived features, we optimize the resulting models, and we compare our results to those obtained in related research. We find that models based on extreme gradient boosting (XGBoost) and multi-layer perceptrons (MLP)perform well in our experiments. Our best models outperform previous comparable research.

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