Efficient Fraud Detection in Ethereum Blockchain through Machine Learning and Deep Learning Approaches

Abstract

Background: This paper tackles the critical challenge of detecting fraudulent transactions within the Ethereum blockchain using machine learning techniques. With the burgeoning importance of blockchain, ensuring its security against fraudulent activities is crucial to prevent significant monetary losses. We utilized a public dataset comprising 9,841 Ethereum transactions, characterized by attributes such as gas price, transaction fee, and timestamp.Methods: Our approach is bifurcated into two core phases: data preprocessing and predictive modeling. In the data preprocessing phase, we meticulously process the dataset and extract pivotal features from transactions, setting the stage for efficient predictive modeling.Findings: For predictive modeling, we employed several machine learning algorithms to discern between fraudulent and legitimate transactions. Our evaluation encompassed algorithms like decision trees, logistic regression, gradient boosting, XGBoost, and an innovative hybrid model that melds random forests with deep neural networks (DNN).Novelty: Our findings underscore that the proposed model boasts a precision rate of 97.16%, marking a substantial leap in fraudulent transaction detection on the Ethereum blockchain in comparison to prevailing methodologies. This paper augments the current efforts aimed at bolstering the security of blockchain transactions using sophisticated analytical strategies..