Hybrid Ensemble Learning Model for Fraud Detection in Ethereum Transactions

Abstract

Ethereum's distributed ledger technology operates on decentralized principles that have transformed how digital assets are exchanged. Yet the platform's underlying design contains security weaknesses that expose users to multiple forms of malicious activity, such as fraudulent wallet schemes, network identity manipulation, pyramid financial structures, and service interruption attacks. This study aims to address the cyber-attacks on the Ethereum platform by developing an innovative ensemble machine-learning framework based on a refined feature set derived from real-time Ethereum transactions. The proposed framework employed a method that encapsulates the feature selection process, leveraging the advantages of machine learning algorithms to derive the refined feature set. The conducted experiment employs Random Forest and XGBoost, to identify the significant features. To mitigate the overfitting issue in the proposed machine learning model, the SMOTEENN (Synthetic Minority Oversampling Edited Nearest Neighbors) technique, has applied. The experimental results reveal that the proposed model exceeds the performance of existing fraud detection approaches, achieving a 99.4% accuracy and a Matthews Correlation Coefficient (MCC) of 94.9%.