Fraud Detection in Decentralized Finance (DeFi) Transactions using ML and Smart Contracts

Abstract

Decentralized Finance (DeFi) has exposed users to sophisticated attacks (flash loans, rug pulls, phishing scams), while conventional, centralized fraud detection methods remain ineffective due to issues of privacy, data fragmentation, and lack of transparency. This paper introduces a novel hybrid framework for real-time fraud detection combining off-chain Machine Learning (ML) intelligence with on-chain smart contract enforcement via decentralized Chainlink oracles. Unlike existing ML systems that only flag behavior post-event, our design enables proactive mitigation, allowing smart contracts to automatically pause suspicious transactions or block malicious wallets based on real-time risk scores. Using an Ethereum dataset ($\approx 1.2$million transactions), the proposed model achieved an F1-score of 95.70% with XGBoost, outperforming traditional algorithms. The framework also demonstrated efficient operation, maintaining an average oracle latency of 1.25 seconds and an on-chain cost of 0.0041 ETH per action. Future work will explore privacy-preserving ML, cross-chain detection, and DAOgoverned explainable AI to improve transparency and trust in DeFi ecosystems.