Securing the Metaverse: The Intersection of ML-Based Oracles and Blockchain Technology

The integration of blockchain and machine learning (ML) in decentralized systems has revolutionized the operational potential of smart contracts, especially in dynamic and interactive environments such as the metaverse. Smart contracts leverage blockchain technology to enable secure, decentralized, and transparent automation of agreements without intermediaries. However, their reliability often depends on external data sources, known as blockchain oracles, which are prone to tampering and manipulation due to their operation outside the blockchain. To address these vulnerabilities, we propose a novel architecture, MARL-RSS (Multi-Agent Reinforcement Learning with Ring Signature Scheme), designed to enhance blockchain security and ensure the trustworthiness of oracle networks. MARL-RSS integrates Multi-Agent Reinforcement Learning techniques to dynamically detect and isolate malicious oracles, ensuring data integrity and system resilience. Furthermore, the integration of a Ring Signature Scheme (RSS) guarantees oracle anonymity and authenticity, enabling secure data sharing while preserving privacy. Experimental results demonstrate the effectiveness of MARL- RSS in detecting malicious oracles with high accuracy while maintaining low false positive rates. The RSS mechanism introduces minimal overhead while ensuring privacy and authenticity. MARL- RSS provides a scalable and secure foundation for decentralized systems, enabling efficient and trustworthy operations in blockchain-based applications, including the metaverse.