Open Access

ARTICLE

Mining Bytecode Features of Smart Contracts to Detect Ponzi Scheme on Blockchain

Xiajiong Shen^1,3, Shuaimin Jiang^2,3, Lei Zhang^1,2,3,*

1 Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, 47500, China
2 Institute of Data and Knowledge Engineering, Henan University, Kaifeng, 47500, China
3 School of Computer and Information Engineering, Henan University, Kaifeng, 47500, China

* Corresponding Author: Lei Zhang. Email:

(This article belongs to the Special Issue: Blockchain Security)

Computer Modeling in Engineering & Sciences 2021, 127(3), 1069-1085. https://doi.org/10.32604/cmes.2021.015736

Received 09 January 2021; Accepted 24 February 2021; Issue published 24 May 2021

Abstract

The emergence of smart contracts has increased the attention of industry and academia to blockchain technology, which is tamper-proofing, decentralized, autonomous, and enables decentralized applications to operate in untrustworthy environments. However, these features of this technology are also easily exploited by unscrupulous individuals, a typical example of which is the Ponzi scheme in Ethereum. The negative effect of unscrupulous individuals writing Ponzi scheme-type smart contracts in Ethereum and then using these contracts to scam large amounts of money has been significant. To solve this problem, we propose a detection model for detecting Ponzi schemes in smart contracts using bytecode. In this model, our innovation is shown in two aspects: We first propose to use two bytes as one characteristic, which can quickly transform the bytecode into a high-dimensional matrix, and this matrix contains all the implied characteristics in the bytecode. Then, We innovatively transformed the Ponzi schemes detection into an anomaly detection problem. Finally, an anomaly detection algorithm is used to identify Ponzi schemes in smart contracts. Experimental results show that the proposed detection model can greatly improve the accuracy of the detection of the Ponzi scheme contracts. Moreover, the F1-score of this model can reach 0.88, which is far better than those of other traditional detection models.

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Keywords

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