<i>MetaPrism</i>: Intelligent Personalized Engagement Policy for User's Security and Privacy in Metaverse Environment

ABSTRACT Early iterations of the metaverse have exposed serious flaws pertaining to user privacy and engagement with other users/entities. Users have been able to collude and block the movement of an individual user, abuse and demean them, and violate their privacy. Such security flaws may hamper the widespread adoption of the metaverse. The bad experiences with largely unregulated social networks, manifesting in fake user profiles, sentiment manipulation through fake news and potential law and order problems, weighs heavily on the future commercial success of the metaverse. Amidst calls for tighter regulation of social media and new metaverse platforms by national governments, the metaverse platform designers would need to address these existing loopholes on priority. This article presents MetaPrism , a novel user‐defined personal engagement policy governing user interactions with other users and entities in the metaverse environment. MetaPrism's unique integration of a user‐defined engagement policy and a deep learning‐based intrusion detection system (DL‐IDS) provides an unprecedented combination of user autonomy and the capability of real‐time threat detection. MetaPrism enables fine‐grained control over how other users and entities engage with an individual user, including the extent and scope of their interactions. Beyond improving individual user safety and privacy, MetaPrism establishes a scalable, flexible model that aligns with emerging regulatory frameworks, ensuring ethical and secure growth of the metaverse. Our proposed approach also encompasses the pressing need for an Intrusion Detection System (IDS) for a virtual assistant, human‐computer interfaces, and Internet of Things (IoT) device‐enabled metaverse environments. By empowering users with comprehensive privacy controls and delivering reliable cyber threat detection, MetaPrism paves the way for broader adoption and trust in metaverse platforms and thus assuages the security concerns with metaverse to a large extent. Experimental results demonstrate that the proposed DL‐IDS achieves a classification accuracy of 95.46% and an F1‐score of 95.8%, ensuring reliable detection of cyber threats such as DDoS, DoS, and reconnaissance attacks. This framework not only mitigates security risks but also fosters a safer, user‐driven ecosystem, ensuring the metaverse realizes its full potential as a transformative digital space.