Machine Learning Approaches for Fraud Detection in Decentralized Finance.

Devised and validated ML/DL frameworks under NUS supervision to detect Ethereum-based fraud, conducted blockchain data exploration and engineered key features to enhance predictive accuracy.

• Achieved high model performance with XGBoost (98% accuracy, 96% recall), Random Forest (97% accuracy, 94% recall), and Echo State Networks (95% accuracy).