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Open Access

ARTICLE

Detecting Ethereum Ponzi Scheme Based on Hybrid Sampling for Smart Contract

Yuanjun Qu, Xiameng Si*, Haiyan Kang, Hanlin Zhou
College of Computer Science, Beijing Information Science and Technology University, Beijing, 100192, China
* Corresponding Author: Xiameng Si. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.057368

Received 15 August 2024; Accepted 18 November 2024; Published online 20 December 2024

Abstract

With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD (LSTM-Transformer smart Ponzi schemes detection), which is a Ponzi scheme detection method that combines Long Short-Term Memory (LSTM) and Transformer considering the time-series transaction information of smart contracts as well as the global information. Based on the verified smart contract addresses, account features, and code features are extracted to construct a feature dataset, and the SMOTE-Tomek algorithm is used to deal with the imbalanced data classification problem. By comparing our method with the other four typical detection methods in the experiment, the LT-SPSD method shows significant performance improvement in precision, recall, and F1-score. The results of the experiment confirm the efficacy of the model, which has some application value in Ethereum Ponzi scheme smart contract detection.

Keywords

Blockchain; smart contract detection; Ponzi scheme; long short-term memory; hybrid sampling

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