[1]毛典辉,梁秀霞,赵 爽,等.面向区块链平台的庞氏骗局模式检测方法[J].计算机技术与发展,2022,32(05):153-159.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 026]
　MAO Dian-hui,LIANG Xiu-xia,ZHAO Shuang,et al.Ponzi Scheme Pattern Detection Method for Blockchain Platform[J].,2022,32(05):153-159.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 026]
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面向区块链平台的庞氏骗局模式检测方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年05期
页码:
153-159
栏目:
应用前沿与综合
出版日期:
2022-05-10

文章信息/Info

Title:
Ponzi Scheme Pattern Detection Method for Blockchain Platform
文章编号:
1673-629X(2022)05-0153-07
作者:
毛典辉; 梁秀霞; 赵 爽; 郝治昊
北京工商大学 计算机学院,北京 100048
Author(s):
MAO Dian-hui; LIANG Xiu-xia; ZHAO Shuang; HAO Zhi-hao
School of Computer,Beijing Technology and Business University,Beijing 100048,China
关键词:
庞氏骗局; 区块链; 以太坊; 智能合约; TF-IDF; 集成学习; Catboost
Keywords:
Ponzi scheme; blockchain; Ethereum; smart contract; TF-IDF; ensemble learning; Catboost
分类号:
TP301
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 05. 026
摘要:
区块链技术的出现给各行各业带来了新的变革,同时也给诈骗提供了新的平台。 作为金融诈骗的代表形式——庞氏骗局借助智能合约在二代区块链上给人们制造了巨大的损失,这不仅影响区块链技术的发展,同时也在一定程度上扰乱了正常的社会经济秩序,因此,对借助区块链技术实施庞氏骗局的相关平台进行监管势在必行。 该文选取区块链平台以太坊作为研究对象,设计了一种基于智能合约混合特征的庞氏骗局检测算法。 首先根据交易主体间的关联特征判断其是否符合庞氏骗局中回报不公平的金字塔交易形式,提取智能合约交易特征;其次根据智能合约的操作代码在庞氏骗局合约和其他合约出现频率设计了一种新的 ITF 算法,提取区分庞氏骗局智能合约的操作码特征;最后采用 Catboost 集成学习算法来训练庞氏骗局检测模型,算法强调多个特征之间的联系,并解决检测算法训练过程中梯度偏差以及预测偏移问题。 与其他算法相比,该算法在庞氏骗局检测上具有较高的准确率( 精确率 = 0. 89、召回率 = 0. 78、F1 值 = 0. 82)
Abstract:
Blockchain technology has brought new changes to all walks of life, but also provides a new platform for fraud. As are presentative form of financial fraud,Ponzi scheme has created huge losses to people on the second-generation blockchain with the help of smart contracts,which? ? ?not only affects the development of blockchain technology,but also disrupts the normal social and economic order to a certain extent. Therefore,? it is imperative to regulate the relevant platform in a Ponzi scheme which use blockchain technology.Ethereum,a blockchain platform, is selected as the research object to design a Ponzi scheme detection algorithm based on the mixed features of smart contracts. Firstly,according to the correlation characteristics of the transaction subjects,it is judged whether it conforms to the pyramid trading form of Ponzi scheme with unfair returns, and the transaction characteristics of smart contract are extracted.Secondly,a new ITF algorithm is designed according to the occurrence frequency of the operation codes of smart contracts in Ponzi scheme contracts and other contracts to extract the characteristics of the operation codes that distinguish the Ponzi scheme smart contracts.Finally,Catboost ensemble learning algorithm is used to train Ponzi scheme detection model. The algorithm emphasizes the relationship between multiple features,and solves the gradient deviation and prediction offset problems in the training process of detection algorithm.Compared with other algorithms,the proposed algorithm has higher accuracy in Ponzi scheme detection( precision rate = 0. 89,recall rate =0. 78,F1 value = 0. 82) .

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更新日期/Last Update: 2022-05-10