Free-Text Keystroke Dynamics for User Authentication
This addresses the problem of secure and convenient user authentication for applications like cybersecurity, though it appears incremental as it builds on existing keystroke dynamics research.
The paper tackled user authentication using keystroke dynamics from free-text by developing a novel feature engineering method that creates image-like transition matrices, achieving state-of-the-art results with a CNN using cutout and a hybrid CNN-RNN model.
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.