The Evolution of Blockchain Security and Examining Machine Learning’s Impact on Ethereum Fraud Detection

Abstract

Blockchain innovation, best embodied by Ethereum, has revolutionized online transactions by making them more transparent and secure. However, the demand for more sophisticated fraudulent schemes increases with wider adoption, calling for more sophisticated fraud detection methods. Therefore, this paper contributes to the area of blockchain security by providing insights to regulators and stakeholders in Ethereum through an analysis of the Machine Learning (ML) models. We compare traditional approaches like logistic regression and decision trees with more advanced techniques like neural networks and ensemble methods. The performance of the model is measured using accuracy, precision, recall, and the ROC curve. The best accuracy of 0.98 is achieved by the optimized XGBoost framework.