CTRF: Ethereum-Based Ponzi Contract Identification
【Author】 He, Xuezhi; Yang, Tan; Chen, Liping
【Source】SECURITY AND COMMUNICATION NETWORKS
【影响因子】1.968
【Abstract】In recent years, blockchain technology has been developing rapidly. More and more traditional industries are using blockchain as a platform for information storage and financial transactions, mainly because of its new characteristics of non-tamperability and decentralization compared with the traditional systems. As a representative of blockchain 2.0, Ethereum has gained popularity upon its introduction. However, because of the anonymity of blockchain, Ethereum has also attracted the attention of some unscrupulous people. Currently, millions of contracts are deployed on Ethereum, many of which are fraudulent contracts deployed by unscrupulous people for profit, and these contracts are causing huge losses to investors worldwide. Ponzi contracts are typical of these contracts, which mainly reward the funds invested by later investors to early investors, and later investors will have no gain. However, although there are some studies for identifying Ponzi contracts on Ethereum, there is some room for progress in the research. Therefore, we propose a method to detect Ponzi scheme contracts on Ethereum-CTRF. This method forms a dataset by extracting the word features and sequence features of the smart contract's code and the features of transactions. The dataset is divided into a training set and a test set. Oversampling is performed on the training set to deal with the problem of positive and negative sample imbalance. Finally, the model is trained on the training set and tested on the test set. The experimental results show that the model has significantly improved recall compared with existing Ponzi contract detection methods.
【Keywords】
【发表时间】2022 MAR 29
【收录时间】2022-07-30
【文献类型】实证性文章
【主题类别】
区块链治理-技术治理-实体分类
wangjiaxin
发表在《SECURITY AND COMMUNICATION NETWORKS》,https://doi.org/10.1155/2022/1554752,本文提出了一种在以太坊上检测庞氏骗局合约的方法。该方法通过提取智能合约代码的词特征、序列特征和交易特征,形成数据集。数据集分为训练集和测试集。对训练集进行过采样,最后对模型进行训练,实验结果表明,与现有的庞氏契约检测方法相比,该模型显著提高了召回率。
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