Continuous and Silent User Authentication Through Mouse Dynamics and Explainable Deep Learning

Over the years the number of fraud credential attacks increased drastically. To avoid this type of attack many types of authentication methods have been introduced, in particular, recently taken hold biometric-based such as physical-biometric and behavior-biometric. The idea at the bottom of the latter is that each person has a unique behavior. Starting from the touch dynamics, and keyboard dynamics nowadays, the main topic of study is mouse dynamics. Unlike the other techniques, mouse dynamics require simpler hardware to capture the biometric data without using sensitive data from the users. In this paper, we propose a method based on mouse dynamics and explainable deep learning for continuous and silent user authentication. We propose four different images obtained starting from mouse dynamics and we submit these images to several deep learning models obtaining interesting results in user behavior detection. Moreover, we propose the adoption of the Gradient-weighted Class Activation Mapping to highlight the areas of the images under study that is responsible for a specific categorization to explain the model decision.