2025/02/27 by Sai Ganesh Grandhi, Grandhi, Sai Ganesh, Saeed Samet +1
Computer Science · #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #User Authentication and Security Systems #Visual Attention and Saliency Detection
paper · pdf · doi:10.48550/arxiv.2502.20359
openalex publication_date 2025/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Traditional authentication methods, such as passwords and biometrics, verify a user's identity only at the start of a session, leaving systems vulnerable to session hijacking. Continuous authentication, however, ensures ongoing verification by monitoring user behavior. This study investigates the long-term feasibility of eye-tracking as a behavioral biometric for continuous authentication in virtual reality (VR) environments, using data from the GazebaseVR dataset. Our approach evaluates three architectures, Transformer Encoder, DenseNet, and XGBoost, on short and long-term data to determine their efficacy in user identification tasks. Initial results indicate that both Transformer Encoder and DenseNet models achieve high accuracy rates of up to 97% in short-term settings, effectively capturing unique gaze patterns. However, when tested on data collected 26 months later, model accuracy declined significantly, with rates as low as 1.78% for some tasks. To address this, we propose periodic model updates incorporating recent data, restoring accuracy to over 95%. These findings highlight the adaptability required for gaze-based continuous authentication systems and underscore the need for model retraining to manage evolving user behavior. Our study provides insights into the efficacy and limitations of eye-tracking as a biometric for VR authentication, paving the way for adaptive, secure VR user experiences.