  Cybersecurity is a crucial step in data protection to ensure user security
and personal data privacy. In this sense, many companies have started to
control and restrict access to their data using authentication systems.
However, these traditional authentication methods, are not enough for ensuring
data protection, and for this reason, behavioral biometrics have gained
importance. Despite their promising results and the wide range of applications,
biometric systems have shown to be vulnerable to malicious attacks, such as
Presentation Attacks. For this reason, in this work, we propose to study a new
approach aiming to deploy a presentation attack towards a keystroke
authentication system. Our idea is to use Conditional Generative Adversarial
Networks (cGAN) for generating synthetic keystroke data that can be used for
impersonating an authorized user. These synthetic data are generated following
two different real use cases, one in which the order of the typed words is
known (ordered dynamic) and the other in which this order is unknown
(no-ordered dynamic). Finally, both keystroke dynamics (ordered and no-ordered)
are validated using an external keystroke authentication system. Results
indicate that the cGAN can effectively generate keystroke dynamics patterns
that can be used for deceiving keystroke authentication systems.
