Authentication and Monitoring of User Identities Based on Mouse Dynamics

User identification and monitoring is one of the most important issues in computer system security.A new method for user identification is presented based on the dynamics of computer mouse behavior.Data of mouse behavior in various applications are collected,and users' mouse behavior in human computer interaction is analyzed and modeled,specifically from both the interaction layer and the physiological layer.Based on the dynamic model,a real-time identity authentication and monitoring prototype system is developed,which can intercept users' mouse behavior data,and compare user's current behavior with his history behavior model in order to detect and authenticate current user's identity.According to the result of authentication,system responds real-time and defends against the intrusion of illegal user.An algorithm that uses feature dimension reduction and neural network for classification is applied in experiments for ten users.The experimental results show that mouse dynamics is effective for authenticating and monitoring user identities with a false accept rate(FAR) of 0.48% and a false rejection rate(FRR) of 2.86%.