计算机科学 ›› 2021, Vol. 48 ›› Issue (1): 273-279.doi: 10.11896/jsjkx.191100020
张艳梅, 楼胤成
ZHANG Yan-mei, LOU Yin-cheng
摘要: 区块链技术的发展吸引了全球投资者的目光。目前,有数以万计的智能合约部署在以太坊上。在给金融、溯源等诸多行业带来颠覆性的创新之余,以太坊上的部分智能合约含有诸如庞氏骗局等欺诈形式,给全球投资者造成了数百万美元的损失。但是,目前针对互联网金融背景下庞氏骗局的定量识别方法较少,针对以太坊上庞氏骗局合约检测的研究较少,且检测精度有进一步提高的空间,文中提出基于深度神经网络的庞氏骗局合约检测方法。该方法提取出智能合约中有助于识别庞氏骗局的特征,如智能合约的操作码特征和账户特征,形成数据集,而后在数据集上训练模型,在测试集上检测性能。实验结果表明,基于深度神经网络的庞氏骗局合约检测方法具有99.6%的查准率和96.3%的查全率,均优于现有方法。
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[1] ZHENG Z B,XIE S A.Blockchain challenges and opportunities:A survey[C/OL]//International Journal of Web and Grid Ser-vices.http://https://xueshu.baidu.com/usercenter/paper/show?paperid=7e00413a964b3b16c3495eb19c64a1f4&site=xueshu_se. [2] SWAN M.Blockchain:Blueprint for a New Economy[M].Newton,MA,USA:O'Reilly Media,2015. [3] Bitcoin:A Peer-to-Peer Electronic Cash System.[OL].https://bitcoin.org/bitcoin.pdf. [4] CoinDesk.Understanding Ethereum-blockchain Research Report [OL].www.coindesk.com/research/understandingethereum-report/. [5] A Next-Generation Smart Contract and Decentralized Application Platform.[OL].https://github.com/ethereumlwiki/wiki/WhitePaper. [6] SZABO N.Smart Contracts:Building Blocks for Digital Markets [OL].http://www.fon.hum.uva.nl/rob/Courses/InformationInSpeech/CDROM/Literature/LOTwinterschool2006/szabo.best.vwh.net/smart_contracts_2.html. [7] BOCEK T.Digital Marketplaces Unleashed[M].Springer-Verlag GmbH.2017-09-15:169-184.ISBN 978-3-662-49274-1. [8] NORTA A.Creation of smart-contracting collaborations for decentralized autonomous organizations[OL].https://link.springer.com/chapter/10.1007%2F978-3-319-21915-8_1. [9] CHRISTIDIS K,DEVETSIKIOTIS M.Blockchains and smartcontracts for the internet of things[C]//IEEE Access.2016:2292-2303. [10] HE P,YU G,ZHANG Y F,et al.Survey on Blockchain Technology and Its Application Prospect[J].Computer Science,2017,44(4):1-7,15. [11] WANG Q G,HE P,NIE T Z,et al.Survey of Data Storage and Query Techniques in Blockchain Systems[J].Computer Science,2018,45(12):12-18. [12] HIGGINS S.SEC Seizes Assets from Alleged Altcoin Pyramid Scheme[OL].https://www.coindesk.com/sec-seizesalleged-altcoin-pyramid-scheme. [13] KEIRNS G.Gemcoin Ponzi Scheme Operator Hit with $74Million Judgment.[OL].https://bitcoinwiki.co/gemcoinponzi-scheme-operator-hit-with-74-million-judgment/. [14] MORRIS D Z.The Rise of Cryptocurrency Ponzi Schemes[OL].https://www.theatlantic.com/technology/archive/2017/05/cryptocurrency-ponzi-schemes/5286. [15] ZHAO M.Identification and prevention of Ponzi scheme under the background of internet finance[J].Zhejiang Finance,2016(8):13-17. [16] CHEN W,ZHENG Z,NGAI E,et al.Exploiting Blockchain Data to Detect Smart Ponzi Schemes on Ethereum[J/OL].IEEE Access,2019:1-1.https://www.researchgate.net/publication/331853833_Exploiting_Blockchain_Data_to_Detect_Smart_Ponzi_Schemes_on_Ethereum. [17] CHEN W,ZHENG Z,CUI J,et al.Detecting ponzi schemes on ethereum:Towards healthier blockchain technology[C]//Proc.World Wide Web Conf.World Wide Web,2018:1409-1418. [18] Wikipedia.PonziScheme[OL].https://en.wikipedia.org/wiki/Ponzi_scheme. [19] YAO L,CHEN W.The Enlightenment of American P2P Supervision[J].China Finance,2015(7):63-64. [20] DENG L,YU D.Deep Learning:Methods and Applications[J].Foundations & Trends in Signal Processing,2014,7(3). [21] LECUN Y,BENGIO Y,HINTON G.Deep learning[OL].https://www.nature.com/articles/nature14539. [22] SCHMIDHUBER,JÜRGEN.Deep Learning in Neural Net-works:An Overview[J].Neural Netw,2015,61:85-117. [23] JIAO L C,YANG S Y,LIU F,et al.Seventy Years Beyond Neural Networks:Retrospect and Prospect[J].Chinese Journal of Computers,2016,39(8):1697-1716. [24] FRANSCOIS C.Deep Learning with Python[M].Beijing:Posts and Telecommunications Press,2018. [25] HUANG L W,JIANG B T,LU S Y,et al.Survey on Deep Learning Based Recommender Systems[J].Chinese Journal of Computers,2018,41(7):1619-1647. [26] LI C,CHAI Y M,NAN X F,et al.Research on Problem Classification Method Based on Deep Learning[J].Computer Science,2016,43(12):115-119. [27] BARTOLETTI M,CARTA S,CIMOLI T,et al.Dissecting ponzi schemes on ethereum:Identification,analysis,and impact[OL].https://arxiv.org/abs/1703.03779. [28] VASEK M,MOORE T.There's No Free Lunch,Even Using Bitcoin:Tracking the Popularity and Profits of Virtual Currency Scams[C]//Springer Berlin Heidelberg.2015:44-61. |
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