Securing the Metaverse: The Intersection of ML-Based Oracles and Blockchain Technology
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".