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    Artificial Intelligence-based cybersecurity for the Metaverse: research challenges and opportunities

    Awadallah, A. and Eledlebi, K. and Zemerly, J. and Puthal, P. and Damiani, E. and Taha, K. and Kim, T.-Y. and Yoo, Paul and Choo, K.-K.R. and Yim, M.-S. and Yeun, C.Y. (2024) Artificial Intelligence-based cybersecurity for the Metaverse: research challenges and opportunities. IEEE Communications Surveys & Tutorials , ISSN 1553-877X.

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    Abstract

    The metaverse, known as the next-generation 3D Internet, represents virtual environments that mirror the physical world. It is supported by innovative technologies such as digital twins and extended reality (XR), which elevate user experiences across various fields. However, the metaverse also introduces significant cybersecurity and privacy challenges that remain underexplored. Due to its complex multi-tech infrastructure, the metaverse requires sophisticated, automated, and intelligent cybersecurity measures to mitigate emerging threats effectively. Therefore, this paper is the first to explore Artificial Intelligence (AI)-driven cybersecurity techniques for the metaverse, examining academic and industrial perspectives. First, we provide an overview of the metaverse, presenting a detailed system model, diverse use cases, and insights into its current industrial status. We then present attack models and cybersecurity threats derived from the unique characteristics and technologies of the metaverse. Next, we review AI-driven cybersecurity solutions based on three critical aspects: User authentication, intrusion detection systems (IDS), and the security of digital assets, specifically for Blockchain and Non-fungible Tokens (NFTs). Finally, we highlight challenges and suggest future research opportunities to enhance metaverse security, privacy, and digital asset transactions.

    Metadata

    Item Type: Article
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Paul Yoo
    Date Deposited: 17 Oct 2024 10:23
    Last Modified: 17 Oct 2024 19:37
    URI: https://eprints.bbk.ac.uk/id/eprint/54402

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