Artificial Intelligence Meets Kinesthetic Intelligence: Mouse-based User Authentication based on Hybrid Human-Machine Learning

Abstract

Current mainstream biometric user authentication approaches are based on passive measurements of the subject's characteristics, and usually come with less-than-satisfactory accuracy. This paper takes a unique approach to biometric authentication. Specifically, instead of training a machine learning algorithm to recognize a legitimate user, the paper proposes a hybrid type of training, in which the legitimate user is also trained to use a customized instance of the machine. The user thus achieves a level of artificially-induced expertise to interact with the machine, which makes the user easier to recognize. We implement this concept in a mouse-based user authentication system, in which we produce customized machine instances by introducing an angle offset to the standard mouse. Human subjects then rely on their kinesthetic intelligence to achieve motor learning and visual-motor adaptation to the modified mouse. We design a 7-week IRB-approved experiment, collect data from 18 human subjects over this period, and evaluate the proposed approach with two existing state-of-the-art mouse-based authentication schemes. We find that, in both schemes, our approach significantly outperforms the baseline in which a regular unaltered mouse is used. Somewhat surprisingly, results also show that our approach improves the authentication performance even when both legitimate and non-legitimate users are trained to exactly the same instance of customized machine (i.e., the same mouse angle offset). In addition, we also observe that users can generally maintain their learned expertise even after one week of washout, which further demonstrates the practicality of the approach. Finally, we present a practical strategy to manage the enrollment of users in such a proposed system.