Free-Text Keystroke Dynamics for User Authentication

Authors

Jianwei Li, San Jose State University
Han Chih Chang, San Jose State University
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_15

First Page

357

Last Page

380

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.

Department

Computer Science

Recommended Citation

Jianwei Li, Han Chih Chang, and Mark Stamp. "Free-Text Keystroke Dynamics for User Authentication" Advances in Information Security (2022): 357-380. https://doi.org/10.1007/978-3-030-97087-1_15