In the realm of Virtual Reality (VR), passwords and pins are primary methods of user authentication for application and device access. Despite the well-documented security vulnerabilities associated with knowledge-based authentication methods, VR devices persist in utilizing them for user authentication. Due to these security vulnerabilities in existing authentication systems on VR devices, there is an increasing demand for more secure and robust authentication methods in the VR ecosystem. In this paper, we introduce HM-Auth, a user-authentication system that verifies a user's identity by leveraging the intrinsic hand movement signatures while users type predefined text on their VR screen. Experiments conducted on the hand movement patterns of 30 volunteer participants demonstrate that our Siamese Networks-based-HM-Auth model could achieve high intra-user and low inter-user similarity scores. The HM-Auth system effectively controls false acceptance by employing our symmetric rejection method, achieving a low False Acceptance Rate (FAR) of 0.08 at a False Reject Rate (FRR) of 0. The experimental analysis results highlight the HM-Auth's potential as a promising and secure authentication approach that is crafted for immersive environments.

This paper introduces a lightweight, low-cost, easy-to-use, and unobtrusive continuous user authentication system based on concealable biometric signals. The proposed authentication model continuously verifies a user’s identity throughout the user session while s/he watches a video or performs free-text typing on his/her desktop/laptop keyboard. The authentication model utilizes unobtrusively recorded electroencephalogram (EEG) signals and learns the user’s unique biometric signature based on his/her brain activity.Our work has multifold impact in the area of EEG-based authentication: (1) a comprehensive study and a comparative analysis of a wide range of extracted features are presented. These features are categorized based on the EEG electrodes placement position on the user’s head, (2) an optimal feature subset is constructed using a minimal number of EEG electrodes, (3) a deep neural network-based user authentication model is presented that utilizes the constructed optimal feature subset, and (4) a detailed experimental analysis on a publicly available EEG dataset of 26 volunteer participants is presented.Our experimental results show that the proposed authentication model could achieve an average Equal Error Rate (EER) of 0.137%. Although a thorough analysis on a larger pool of subjects must be performed, our results show the viability of low-cost, lightweight EEG-based continuous user authentication systems.

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.