Real-Time Fraud Prevention in Decentralized Finance Through Oracle-Mediated Machine Learning

Abstract

Blockchain-based financial systems process billions in transactions but remain vulnerable to sophisticated fraud schemes. Current detection approaches analyze completed transactions, preventing neither fund loss nor protocol exploitation. We address this through an oracle-mediated prevention system integrating machine learning inference with smart contract execution. Training ensemble models on 12,847 Ethereum transactions with engineered features capturing gas anomalies and temporal patterns, we achieve 94.2\% fraud classification accuracy. Testnet deployment demonstrates 1.09-second response latency with 6.8\% computational overhead, contrasting favorably against prior on-chain implementations requiring 34\% overhead. Our working prototype validates practical viability for production environments where security requirements justify marginal transaction costs.