Open Access

ARTICLE

Ponzi Scheme Detection for Smart Contracts Based on Oversampling

Yafei Liu^1,2, Yuling Chen^1,2,*, Xuewei Wang³, Yuxiang Yang², Chaoyue Tan²

1 State Key Laboratory of Public Big Data, Guizhou University, Guiyang, 550025, China
2 College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China
3 Computer College, Weifang University of Science and Technology, Weifang, 262700, China

* Corresponding Author: Yuling Chen. Email:

Computers, Materials & Continua 2026, 86(1), 1-21. https://doi.org/10.32604/cmc.2025.069152

Received 16 June 2025; Accepted 25 August 2025; Issue published 10 November 2025

Abstract

As blockchain technology rapidly evolves, smart contracts have seen widespread adoption in financial transactions and beyond. However, the growing prevalence of malicious Ponzi scheme contracts presents serious security threats to blockchain ecosystems. Although numerous detection techniques have been proposed, existing methods suffer from significant limitations, such as class imbalance and insufficient modeling of transaction-related semantic features. To address these challenges, this paper proposes an oversampling-based detection framework for Ponzi smart contracts. We enhance the Adaptive Synthetic Sampling (ADASYN) algorithm by incorporating sample proximity to decision boundaries and ensuring realistic sample distributions. This enhancement facilitates the generation of high-quality minority class samples and effectively mitigates class imbalance. In addition, we design a Contract Transaction Graph (CTG) construction algorithm to preserve key transactional semantics through feature extraction from contract code. A graph neural network (GNN) is then applied for classification. This study employs a publicly available dataset from the XBlock platform, consisting of 318 verified Ponzi contracts and 6498 benign contracts. Sourced from real Ethereum deployments, the dataset reflects diverse application scenarios and captures the varied characteristics of Ponzi schemes. Experimental results demonstrate that our approach achieves an accuracy of 96%, a recall of 92%, and an F1-score of 94% in detecting Ponzi contracts, outperforming state-of-the-art methods.

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Keywords

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