A Continuous Authentication Framework for Securing Metaverse Identities

Abstract

In the Metaverse, continuous authentication is essential for verifying the ongoing connection between a user’s physical identity and avatar, ensuring secure access to various services. This process is crucial for confirming identities, maintaining security, and preventing unauthorized activities that could compromise legitimate services. However, traditional biometric-based authentication methods are susceptible to threats such as impersonation, replay attacks, and disguise, primarily due to the difficulty in directly using biometric information to represent the connection between virtual and physical identities. To address these challenges, some studies have proposed using blockchain schemes to mitigate security threats. Despite this, these approaches often encounter issues like insufficient network protection for authentication connections, prolonged data processing times, and latency. To overcome these limitations, we propose a secure continuous authentication framework that leverages standard protocols such as QUIC and JWT to verify user identities efficiently. Our approach employs embedding models on edge devices to generate and transmit biometric data. In contrast, a deep learning-based model on the server validates the user’s credentials, ensuring both high performance and availability. Experimental results show that our QUIC and JWT-based protocol delivers superior security and effectiveness compared to traditional biometric approaches and blockchain-based methods, achieving an AUC of 0.97, an EER of 3.77, and an F1 score of 0.96.