Development of an Optimized Hybrid XGBoost–GRU Model for Detection of Ponzi Schemes in Ethereum Transaction Networks

Abstract

Blockchain technology, particularly Ethereum, has revolutionized decentralized finance by enabling transparent, secure, and programmable smart contracts. However, these same features have created avenues for financial crimes such as Ponzi schemes, where fraudulent actors exploit pseudonymity and the absence of centralized oversight to deceive investors. This study develops an optimized hybrid detection model that combines eXtreme Gradient Boosting (XGBoost) and Gated Recurrent Units (GRU) to identify Ponzi schemes in Ethereum transaction networks. The model integrates XGBoost’s capability for structured feature learning with GRU’s temporal sequence modeling to capture both static and dynamic behavioral patterns of smart contracts. Using a dataset of 3,866 labeled Ethereum contracts obtained from Kaggle, the research employed advanced preprocessing, temporal sequence enrichment, and class balancing through SMOTE-TS to mitigate data imbalance. Bidirectional optimization, incorporating attention-enhanced GRUs and Bayesian hyperparameter tuning for XGBoost, further improved learning performance and generalization. The model was evaluated using precision, recall, F1-score, ROC-AUC, and PR-AUC, achieving higher detection accuracy of 99% (F1-score = 0.945, ROC-AUC = 0.983) than standalone XGBoost or GRU models. Results demonstrate the hybrid model’s superior ability to detect temporal and statistical anomalies, reducing false negatives and improving early detection of fraudulent contracts. The approach contributes a scalable and interpretable framework for real-time Ponzi detection in blockchain ecosystems. This research not only enhances the reliability of Ethereum’s financial ecosystem but also offers regulators and developers a novel tool for proactive fraud prevention. Future work could extend this framework to multi-chain detection systems and real-time forensic monitoring.