Continuous and Silent User Authentication Through Mouse Dynamics and Explainable Deep Learning: A Proposal

Abstract

Over the years, the number of compromised accounts dramatically increased. To avoid this type of attack various types of authentication methods were introduced. In particular, currently, researchers are focusing on biometric-based techniques such as physical-biometric and behavior-biometric. The idea at the bottom of the last technique is that each person exhibits a unique behavior. Starting from the touch dynamics, and keyboard dynamics nowadays, one of the most promising investigation areas is currently represented by mouse dynamics. Because of the simpler technology necessary to gather biometric data without employing user sensor data, the latter has recently been a popular study area. In this paper, we propose an approach for continuous and silent user authentication based on mouse dynamics and explainable deep learning. We build a set of images using an existing dataset of mouse dynamics in CSV format. The images obtained were then used to train a deep-learning model to discriminate between legitimate and malicious users. We also adopted the Gradient-weighted Class Activation Mapping, to allow highlighting the areas of the images which are responsible for a specific legitimate/attack prediction, thus providing explainability behind the model classification. The preliminary experimental analysis based on ten different users shows that the proposed method can be promising in silent and continuous user authentication.