A Temporal Memory-based Continuous Authentication System

Abstract

With the emerging use of technology, verifying a user’s identity continuously throughout a device’s usage has become increasingly important. This paper proposes an authentication system that unobtrusively verifies a user’s identity continuously, based on his/her hand movement patterns captured using accelerometer, while a user performs free-text typing. Our model validates a user’s identity with a verification decision in every ≈ 20ms interval. The authentication model utilizes a short temporal memory of size M of a user’s hand movement patterns. Experiments on different values of M suggests that the model shows an improved and consistent performance by increasing the size of the temporal memory of a user’s hand movement patterns to M ≈ 300ms.The authentication system requires only a user’s hand movement signals in order to authenticate a user on a device. Experiments on the hand movement patterns of 27 volunteer participants, captured using motion sensors of a Sony Smartwatch while they performed free-text typing on a desktop/laptop device, show that our model could achieve an average authentication accuracy of 99.8% with an average False Accept Rate (FAR) of 0.0003 and an average False Reject Rate (FRR) of 0.0034.