Machine Learning in Metaverse Security: Current Solutions and Future Challenges

Abstract

The Metaverse, positioned as the next frontier of the internet, has the ambition to forge a virtual shared realm characterized by immersion, hyper spatiotemporal dynamics, and self-sustainability. Recent technological strides in AI, Extended Reality (XR), 6G, and blockchain propel the Metaverse closer to realization, gradually transforming it from science fiction into an imminent reality. Nevertheless, the extensive deployment of the Metaverse faces substantial obstacles, primarily stemming from its potential to infringe on privacy and be susceptible to security breaches, whether inherent in its underlying technologies or arising from the evolving digital landscape. Metaverse security provisioning is poised to confront various foundational challenges owing to its distinctive attributes, encompassing immersive realism, hyper spatiotemporally, sustainability, and heterogeneity. This paper undertakes a comprehensive study of the security and privacy challenges facing the Metaverse, leveraging Machine Learning (ML) models for this purpose. In particular, our focus centers on an innovative distributed Metaverse architecture characterized by interactions across 3D worlds. Subsequently, we conduct a thorough review of the existing cutting-edge measures designed for Metaverse systems while also delving into the discourse surrounding security and privacy threats. As we contemplate the future of Metaverse systems, we outline directions for open research pursuits in this evolving landscape.