Heterogeneous ensemble model for enhanced abnormal account detection in the Ethereum Blockchain Ecosystem

Abstract The global expansion of blockchain technology has unfortunately been accompanied by a rise in fraudulent activities within decentralized applications (DApps), leading to substantial financial losses. The immense volume of transaction data (big data) makes manual detection of abnormal account behavior impossible, necessitating the use of automated machine learning (ML) techniques. Existing anomaly machine learning detection approaches often rely on single-classifier models that suffer from limited generalization, high false-positive rates, or insufficient feature relevance, thereby compromising detection accuracy and system security. Moreover, the high dimensionality and complexity of blockchain data necessitate more sophisticated and robust methodologies that can effectively identify relevant features and leverage the strengths of multiple learning algorithms. This study addresses a key gap by proposing a novel anomaly detection framework for the Ethereum blockchain that distinctively integrates the Boruta feature selection algorithm with a combination of ensemble methods and a fuzzy logic classifier. Specifically, we investigate the performance of various ensemble techniques (bagging, boosting, voting, and stacking) combined with foundational models (Decision Tree, Random Forest, K-Nearest Neighbors, and XGBoost), including a specialized Fuzzy ENORA model. The objective is to significantly enhance the accuracy of anomaly detection. Our results demonstrate that the ensemble models consistently and significantly outperformed single-classifier models, achieving a mean performance metric of 0.99 across accuracy, precision, recall, and F1 score, affirming the robustness of the proposed Boruta-driven ensemble approach for securing blockchain transactions.