Computer Science ›› 2021, Vol. 48 ›› Issue (1): 273-279.doi: 10.11896/jsjkx.191100020

• Information Security • Previous Articles     Next Articles

Deep Neural Network Based Ponzi Scheme Contract Detection Method

ZHANG Yan-mei, LOU Yin-cheng   

  1. Information School,Central University of Finance and Economics,Beijing 100081,China
  • Received:2019-11-03 Revised:2019-12-27 Online:2021-01-15 Published:2021-01-15
  • About author:ZHANG Yan-mei,born in 1976,Ph.D,professor,is a member of China Compu-ter Federation.Her main research interests include business intelligence,ser-vice computing and blockchain.
  • Supported by:
    National Natural Science Foundation of China(61602536,61773415,61672104).

Abstract: The development of blockchain technology has attracted the attention of global investors.Currently,tens of thousands of smart contracts are deployed on Ethereum.In spite of bringing disruptive innovation to finance,traceability and many other industries,some smart contracts on Ethereum contain fraudulent forms such as Ponzi schemes,causing millions of dollars of losses to global investors.However,at present,there are few quantitative identification methods for Ponzi scheme under the background of Internet finance,few researches on detection of Ponzi scheme contract on Ethereum,and the detection accuracy needs to be improved.Therefore,a Ponzi scheme contract detection method based on deep neural network is proposed.It extracts the features of smart contract that are helpful to identify Ponzi scheme,such as operation code features and account features,to form a data set.Then,the model is trained on the dataset and performance is tested on test set.The experimental results show that the Ponzi scheme contract detection method based on deep neural network has a precision of 99.6% and a recall rate of 96.3%,which are better than that of existing methods.

Key words: Blockchain, Deep neural network, Ethereum, Ponzi scheme, Smart contract

CLC Number: 

  • TP309.2
[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.
[1] WANG Zi-kai, ZHU Jian, ZHANG Bo-jun, HU Kai. Research and Implementation of Parallel Method in Blockchain and Smart Contract [J]. Computer Science, 2022, 49(9): 312-317.
[2] HUANG Song, DU Jin-hu, WANG Xing-ya, SUN Jin-lei. Survey of Ethereum Smart Contract Fuzzing Technology Research [J]. Computer Science, 2022, 49(8): 294-305.
[3] LI Bo, XIANG Hai-yun, ZHANG Yu-xiang, LIAO Hao-de. Application Research of PBFT Optimization Algorithm for Food Traceability Scenarios [J]. Computer Science, 2022, 49(6A): 723-728.
[4] ZHOU Hang, JIANG He, ZHAO Yan, XIE Xiang-peng. Study on Optimal Scheduling of Power Blockchain System for Consensus Transaction ofEach Unit [J]. Computer Science, 2022, 49(6A): 771-776.
[5] FU Li-yu, LU Ge-hao, WU Yi-ming, LUO Ya-ling. Overview of Research and Development of Blockchain Technology [J]. Computer Science, 2022, 49(6A): 447-461.
[6] GAO Jian-bo, ZHANG Jia-shuo, LI Qing-shan, CHEN Zhong. RegLang:A Smart Contract Programming Language for Regulation [J]. Computer Science, 2022, 49(6A): 462-468.
[7] WEI Hong-ru, LI Si-yue, GUO Yong-hao. Secret Reconstruction Protocol Based on Smart Contract [J]. Computer Science, 2022, 49(6A): 469-473.
[8] MAO Dian-hui, HUANG Hui-yu, ZHAO Shuang. Study on Automatic Synthetic News Detection Method Complying with Regulatory Compliance [J]. Computer Science, 2022, 49(6A): 523-530.
[9] WEI Hui, CHEN Ze-mao, ZHANG Li-qiang. Anomaly Detection Framework of System Call Trace Based on Sequence and Frequency Patterns [J]. Computer Science, 2022, 49(6): 350-355.
[10] WANG Si-ming, TAN Bei-hai, YU Rong. Blockchain Sharding and Incentive Mechanism for 6G Dependable Intelligence [J]. Computer Science, 2022, 49(6): 32-38.
[11] SUN Hao, MAO Han-yu, ZHANG Yan-feng, YU Ge, XU Shi-cheng, HE Guang-yu. Development and Application of Blockchain Cross-chain Technology [J]. Computer Science, 2022, 49(5): 287-295.
[12] YANG Zhen, HUANG Song, ZHENG Chang-you. Study on Crowdsourced Testing Intellectual Property Protection Technology Based on Blockchain and Improved CP-ABE [J]. Computer Science, 2022, 49(5): 325-332.
[13] REN Chang, ZHAO Hong, JIANG Hua. Quantum Secured-Byzantine Fault Tolerance Blockchain Consensus Mechanism [J]. Computer Science, 2022, 49(5): 333-340.
[14] GAO Jie, LIU Sha, HUANG Ze-qiang, ZHENG Tian-yu, LIU Xin, QI Feng-bin. Deep Neural Network Operator Acceleration Library Optimization Based on Domestic Many-core Processor [J]. Computer Science, 2022, 49(5): 355-362.
[15] JIAO Xiang, WEI Xiang-lin, XUE Yu, WANG Chao, DUAN Qiang. Automatic Modulation Recognition Based on Deep Learning [J]. Computer Science, 2022, 49(5): 266-278.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!