Advanced Genetic Algorithm and Penalty Fitness Function for Enhancing DeFi Security and Detecting Ethereum Fraud Transactions

Resumen

With the shift from Centralized Finance (CeFi) to Decentralized Finance (DeFi), financial transactions have become trustless and self-executing through blockchain platforms, creating new opportunities while exposing the ecosystem to significant fraud risks. However, due to the lack of centralized oversight and the vulnerabilities in the blockchain platforms, DeFi transactions still face several security challenges, including fraud, identity theft, insider threats, and data breaches. Various methods, including regulatory frameworks, machine learning (ML), and deep learning (DL) techniques, are employed to detect these threats, particularly fraud, in DeFi transactions. Although these approaches help identify fraudulent activities, they face challenges related to accuracy and zero-day attacks due to insufficient data and the complexity of emergingfraud patterns. This study presents a novel approach for detecting and profiling fraud attacks, including zero-day ones in DeFi transactions, thereby eliminating the reliance on wallet transaction history, a limitation that previous research has heavily depended on. The proposed approach leverages two key components: a novel analyzer named DeFiTransLyzer (V1.0) and an Advanced Genetic Algorithm (AGA) for fraud transaction profiling. DeFiTransLyzer extracts 79 features from transaction and wallet data. At the same time, the AGA incorporates advanced techniques, including Penalized Fitness Evaluation, Elite Retention Strategy, Dynamic Mutation Rate, and dynamic generation, to create precise fraud profiles. By focusing solely on transaction features, the model ensures that all fraudulent activities, including zero-day ones, initiated within the first transaction of a new account can be effectively detected, without relying on prior wallet activity. To address the scarcity of comprehensive validation datasets, we introduce BCCCDeFiFraudTrans-2025, which comprises 1,026,867 annotated Ethereum transaction samples from the DeFi ecosystem. Additionally, the study establishes two taxonomies for systematic classification, covering the literature on fraud detection and profiling methods. Experimental results demonstrate that the proposed method achieves superior accuracy, precision, and efficiency while offering interpretability through its profiling mechanism. These promising outcomes highlight the potential of AGA profiling to enhance the detection and identification of fraudulent activities, including zero-day ones within DeFi transactions, contributing to the security and resilience of blockchainbased financial systems.