Interpretable Deep Tabular Learning for Fraud and Phishing Detection in Decentralized Finance (DeFi)

Abstract

Decentralized Finance (DeFi) has introduced new security challenges due to its open, permissionless, and pseudonymous nature, which has increased the risk of fraud and phishing activities. This thesis first presents a comprehensive study of 284 DeFi platforms to examine their architectural, functional, and security-related characteristics. Building on this ecosystem-level analysis, the thesis develops a behavior-centric multiclass detection framework using Ethereum transaction data. The framework integrates legitimate, fraud, and phishing activities into a unified dataset and evaluates several traditional and deep tabular learning models, including TabNet, GANDALF, and NODE. The results show that deep tabular models outperform conventional baselines, with NODE achieving the strongest overall performance. Feature importance analysis highlights gas usage, nonce behavior, transaction frequency, and wallet activity as key indicators of malicious behavior. Overall, this thesis demonstrates that behavior-based Ethereum transaction features combined with deep tabular learning can support more effective and scalable DeFi threat detection.