  The development of active and passive biometric authentication and
identification technology plays an increasingly important role in
cybersecurity. Keystroke dynamics can be used to analyze the way that a user
types based on various keyboard input. Previous work has shown that user
authentication and classification can be achieved based on keystroke dynamics.
In this research, we consider the problem of user classification based on
keystroke dynamics features collected from free-text. We implement and analyze
a novel a deep learning model that combines a convolutional neural network
(CNN) and a gated recurrent unit (GRU). We optimize the resulting model and
consider several relevant related problems. Our model is competitive with the
best results obtained in previous comparable research.
