Title
Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics
Publication Date
1-1-2022
Document Type
Contribution to a Book
Publication Title
Advances in Information Security
Volume
54
DOI
10.1007/978-3-030-97087-1_13
First Page
309
Last Page
329
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.
Department
Computer Science
Recommended Citation
Han Chih Chang, Jianwei Li, Ching Seh Wu, and Mark Stamp. "Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics" Advances in Information Security (2022): 309-329. https://doi.org/10.1007/978-3-030-97087-1_13
