Keystroke Presentation Attack: Generative Adversarial Networks for replacing user behaviour

Abstract

Digital security has become crucial in this new era of technology and biometry is becoming a natural and reliable authentication system. In recent years, keystroke dynamics, a type of behavioral biometric, has been used for user authentication and attack detection. In this study, we pursue a new approach to keystroke dynamics data generation focused on the impersonation of a user at the identification stage using Conditional Generative Adversarial Networks (cGAN). To that aim, three different architectures have been designed, implemented, and validated: a Vanilla-cGAN based on simple Neural Networks (NN), an LSTM-cGAN based on Recurrent Neural Networks using Long Short-Term Memory units (LSTM), and a CNN-cGAN based on Convolutional Neural Networks. These models have been validated in two different conditions, one in which the attacker knows exactly the order of the typed words for replicating the behavior and the other in which the order is unknown. To validate the data generated by these models, beyond the internal discriminator’s accuracy, a pre-trained Siamese Network has been used to detect whether two keystroke sequences belong to the same or not. This study suggests that the keystroke dynamics of a user can be successfully imitated via keystroke dynamics data generation using cGANs with different architectures.