Machine Learning and Deep Learning for Fixed-Text Keystroke Dynamics

Authors

Han Chih Chang, San Jose State University
Jianwei Li, San Jose State University
Ching Seh Wu, San Jose State UniversityFollow
Mark Stamp, San Jose State UniversityFollow

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