Fuzzy logic and biometric-based lightweight cryptographic authentication for metaverse security

As Metaverse advances, robust authentication systems become more critical. Traditional security measures are not efficient and adaptable for metaverse scenarios. In this context, this study proposed a two-phase security framework that combines fuzzy logic and convolutional neural network (CNN) techniques for biometric authentication and a lightweight Cryptographic protocol for securing the Metaverse. In the first stage, the CNN model is used to extract accurate biometric signatures from hand tremor data. This innovative method takes into account the inherent unpredictability of biometrics. During the second phase, a cryptographic scheme is used to provide safe mutual authentication between the user and the Metaverse infrastructure. Our findings, which have been verified by BAN Logic analysis, show that the proposed framework achieves a high level of accuracy in user authentication and has robust defensive capabilities against typical cyber-attacks.