Behavioural biometrics provides an extra layer of security for user authentication mechanisms. Among behavioural biometrics, mouse dynamics provides a non-intrusive layer of security. In this paper we propose a novel convolutional neural network for extracting the features from the time series of users’ mouse movements. The effect of two preprocessing methods on the performance of the proposed architecture were evaluated. Different training types of the model, namely transfer learning and training from scratch, were investigated. Results for both authentication and identification systems are reported. The Balabit public data set was used for performance evaluation, however for transfer learning we used the DFL data set. Comprehensive experimental evaluations suggest that our model performed better than other deep learning models. In addition, transfer learning contributed to the better performance of both identification and authentication systems.

No abstract

The growing interest in bot detection can be attributed to the fact that fraudulent actions performed by bots cause surprisingly high economical damage. State-of-the-art bots aim at mimicking as many as possible aspects of human behavior, ranging from response times and typing dynamics to humanlike phrasing and mouse trajectories. In order to support research on bot detection, in this paper, we propose an approach to generate human-like mouse trajectories, called SapiAgent. To implement SapiAgent, we employ deep autoencoders and a novel training algorithm. We performed experiments on our publicly available SapiMouse dataset which contains human mouse trajectories collected from 120 subjects. The results show that SapiAgent is able to generate more realistic mouse trajectories compared with Bézier curves and conventional autoencoders.