  Among user authentication methods, behavioural biometrics has proven to be
effective against identity theft as well as user-friendly and unobtrusive. One
of the most popular traits in the literature is keystroke dynamics due to the
large deployment of computers and mobile devices in our society. This paper
focuses on improving keystroke biometric systems on the free-text scenario.
This scenario is characterised as very challenging due to the uncontrolled text
conditions, the influence of the user's emotional and physical state, and the
in-use application. To overcome these drawbacks, methods based on deep learning
such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks
(RNNs) have been proposed in the literature, outperforming traditional machine
learning methods. However, these architectures still have aspects that need to
be reviewed and improved. To the best of our knowledge, this is the first study
that proposes keystroke biometric systems based on Transformers. The proposed
Transformer architecture has achieved Equal Error Rate (EER) values of 3.84\%
in the popular Aalto mobile keystroke database using only 5 enrolment sessions,
outperforming by a large margin other state-of-the-art approaches in the
literature.
