User Authentication from Mouse Movement Data Using Multi Classifier

Abstract

User authentication is a process to verify the identity of someone who connects to system or resource. There are many technologies to authenticate a user. The biometric authentication system is becoming very popular due to its unique characteristics. This thesis presents a user authentication system from the mouse movement data. The mouse movement data are captured using our own developed user interface and two tools named Jitbit macro reader and Recording User Input (RUI). Raw data are sampled into blocks in two ways: one is based on specific number of action and another is based on specific duration. These data blocks are stored in database. From each blocks, twelve features are generated: Number of Points in the Trajectory, Delay Time, Number of Delay, Number of Action, Standard Deviation of Trajectory Length, Total Length of Trajectory, Standard Deviation of Slope, Standard Deviation of Difference Between Each of Slopes, Number of Curvatures, Curvature of Trajectory, Number of Changes in Horizontal Position and Number of Changes in Vertical Position. This system uses three classifiers: Support Vector Machine, K-Nearest Neighbor and Naïve Bayes separately to verify the proposed authentication system. The system is trained and tested using our captured dataset of 10 users and a benchmark dataset of 28 users. The experimental result shows that K-Nearest Neighbor based classifier performs better in terms of Average Receiver Operating Characteristic (ROC) Area, False Acceptance Rate (FAR) and False Rejection Rate (FRR). We have found FAR=2.78 and FRR=0 by using our collected own data and FAR=1 and FRR=1.2 by using benchmark data. This system is compared with S. Suganya, G. Muthumari, and C. Balasubramanian’s research and found that both FAR and FRR is improved.