Evaluating the long-term viability of eye-tracking for continuous authentication in virtual reality
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917939595182080 |
|---|---|
| author | Grandhi, Sai Ganesh Samet, Saeed |
| author_facet | Grandhi, Sai Ganesh Samet, Saeed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20359 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating the long-term viability of eye-tracking for continuous authentication in virtual reality Grandhi, Sai Ganesh Samet, Saeed Cryptography and Security 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. |
| title | Evaluating the long-term viability of eye-tracking for continuous authentication in virtual reality |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2502.20359 |