基于深度残差网络的庞氏骗局合约检测方法
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(安徽理工大学 计算机科学与工程学院,安徽 淮南 232001)

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葛 斌 (1975-),男,博士,教授,硕士生导师,主要从事信息安全、物联网方面的研究.

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国家自然科学基金(6210071479)、国家重点研发计划(2020YFB1314103) 和安徽省自然科学基金 (2108085QF258) 资助项目


Ponzi scheme contract detection method based on deep residual network
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(School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, Anhui 232001, China)

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    摘要:

    现有针对庞氏骗局智能合约的检测方法大多基于合约操作码特征和账户特征,对初步部署合约检测效果一般。对此,提出一种基于深度残差网络的庞氏骗局合约检测方法。首先,通过分析智能合约操作码特点,提出单点词嵌入编码算法(single word embedding coding algorithm,SWEC) ,对智能合约进行重新编码。然后,利用关键操作码提取方法,提取关键操作码(critical operation code,CO) 及权重值,并以此设计关键操作码权重模块,改进深度残差网络用于合约检测。最后,在公开数据集上进行相关实验,实验结果表明,基于深度残差网络的庞氏骗局合约检测方法具有99.7%的查准率和99.9%的查全率,相比现有方法有较大提升,能够更加准确地识别庞氏骗局合约。

    Abstract:

    The existing detection methods for Ponzi scheme smart contract are mostly based on the operation code features and account features,but using these methods to detect initially deployed contracts is ineffective.Therefore,a Ponzi scheme contract detection method based on deep residual network was proposed.Firstly,by analyzing the characteristics of smart contract,the single word embedding coding algorithm (SWEC) was proposed.Then the contract was recoded by this algorithm.Secondly,critical operation code and its weight were extracted and the weight module of critical operation code (CO) was designed to improve the deep residual network.Finally,the experiments were carried out on public data sets,the experimental results show that the Ponzi scheme contract detection based on deep residual network had 99.7% precision and 99.9% recall.Compared with the existing methods,Ponzi scheme contract was detected more accurately.

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引用本文

葛斌,袁政,任萍,彭曦晨,夏晨星.基于深度残差网络的庞氏骗局合约检测方法[J].光电子激光,2023,34(8):882~889

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  • 收稿日期:2022-05-12
  • 最后修改日期:2022-07-17
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  • 在线发布日期: 2023-08-18
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