Artificial Intelligence-Based Cybersecurity for the Metaverse: Research Challenges and Opportunities

Abstract

The metaverse connects immersive interfaces, virtual environments, artificial intelligence, extended reality, cloud and edge computing, digital twins, communication networks, and blockchain-based services. These technologies can support education, entertainment, commerce, training, and digital ownership, but they also create a broad, evolving cybersecurity surface. Identity theft, privacy loss, abnormal network activity, deepfake-based impersonation, and digital-asset fraud can occur across interconnected layers. This paper presents an independently written, student-oriented study of AI-assisted cybersecurity for the metaverse. It focuses on intelligent authentication, AI-based intrusion detection, privacy-aware learning, and protection of blockchain and NFT ecosystems. The paper describes a layered framework in which relevant security data are collected, processed with suitable AI methods, converted into risk decisions, and followed by proportionate responses. The study also discusses data quality, latency, interoperability, privacy, model attacks, and the need for human oversight. The central view is that AI should complement established cybersecurity controls rather than replace them.