Ponzi scheme detection via oversampling-based Long Short-Term Memory for smart contracts

The application of blockchain technology is growing rapidly, which has aroused great attention in the academic and industrial fields. Based on blockchain 2.0, Ethereum is a mainstream smart contract development and operation platform. The trading process of Ethereum users is facing a serious threat...

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Bibliographic Details
Published in:Knowledge-based systems 2021-09, Vol.228, p.107312, Article 107312
Main Authors: Wang, Lei, Cheng, Hao, Zheng, Zibin, Yang, Aijun, Zhu, Xiaohu
Format: Article
Language:English
Subjects:
Blockchain
Contracts
Cryptography
Ethereum
Long Short-Term Memory (LSTM)
Machine learning
Oversampling
Ponzi schemes
Pyramid operations
Securities fraud
Smart contract
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Summary:The application of blockchain technology is growing rapidly, which has aroused great attention in the academic and industrial fields. Based on blockchain 2.0, Ethereum is a mainstream smart contract development and operation platform. The trading process of Ethereum users is facing a serious threat of financial fraud. In particular, the Ponzi scheme is a classic form of fraud. Relevant works have investigated the issue of Ponzi schemes smart contract detection on Ethereum based on machine learning approaches. Nevertheless, the detection approaches still fall short in dealing with the big data-space Ponzi scheme smart contract detection application based on the class-imbalanced training data. We propose PSD-OL, a Ponzi schemes detection approach based on oversampling-based Long Short-Term Memory (LSTM) for smart contracts in this paper. PSD-OL takes the contract account features and the contract code features together into consideration. Oversampling technique is utilized to fill the class-imbalanced Ponzi scheme smart contracts’ sample feature data. An LSTM model is trained by learning from the feature data for future Ponzi scheme detection. Experimental results conducted on the well-known XBlock dataset demonstrate the effectiveness of the proposed method.
ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.107312