Optimizing Academic Certificate Management With Blockchain and Machine Learning: A Novel Approach Using Optimistic Rollups and Fraud Detection

Abstract

Blockchain technology has brought a significant advancement in the development of academic certificate management systems by enhancing security, transparency, and decentralization. However, challenges such as certificate revocation, transaction costs, and latency still persist. This research proposes a novel mechanism combining smart contracts and Optimistic Rollups technique to address these issues. By leveraging the off-chain processing feature of Optimistic Rollups, the research has significantly reduced transaction latency and costs in certificate revocation. This integration not only optimizes performance but also maintains transparency and data integrity on the blockchain. Moreover, integrating machine learning for fraud detection not only reinforces the security of the certificate management system but also provides timely alerts before fraudulent transactions occur. The combination of blockchain to ensure decentralization and security, along with machine learning to detect and prevent fraud, creates a comprehensive and advanced certificate management system. The experimental outcomes validate the effectiveness of Optimistic Rollups in certificate revocation, showing a notable approximately 61.92% reduction in both transaction costs and latency. Moreover, the machine learning model displays impressive performance, achieving high accuracy in detecting fraudulent users, with an average F1-score of 99.42% and an AUC score nearing perfection. These results underscore the comprehensive and advanced nature of the certificate management system.

Keywords

• Credential revocation management
• smart contract
• Layer-2
• optimistic rollups
• machine learning
• fraud detection