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, Ethereum, Smart contract, Ponzi scheme, Deep neural network

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://
[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].
[4] CoinDesk.Understanding Ethereum-blockchain Research Report [OL]
[5] A Next-Generation Smart Contract and Decentralized Application Platform.[OL].
[6] SZABO N.Smart Contracts:Building Blocks for Digital Markets [OL].
[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].
[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].
[13] KEIRNS G.Gemcoin Ponzi Scheme Operator Hit with $74Million Judgment.[OL].
[14] MORRIS D Z.The Rise of Cryptocurrency Ponzi Schemes[OL].
[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.
[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].
[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].
[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].
[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] SHAO Wei-hui, WANG Ning, HAN Chuan-feng, XU Wei-sheng. Integrated Emergency-Defense System Based on Blockchain [J]. Computer Science, 2021, 48(1): 287-294.
[2] LI Ying, YU Ya-xin, ZHANG Hong-yu, LI Zhen-guo. High Trusted Cloud Storage Model Based on TBchain Blockchain [J]. Computer Science, 2020, 47(9): 330-338.
[3] LIU Shuai, GAN Guo-hua, LIU Ming-xi, FANG Yong, WANG Shou-yang. Multi-subblock Incentive Consensus Mechanism Based on Topology and Distribution Mechanism [J]. Computer Science, 2020, 47(7): 268-277.
[4] LU Ge-hao, XIE Li-hong and LI Xi-yu. Comparative Research of Blockchain Consensus Algorithm [J]. Computer Science, 2020, 47(6A): 332-339.
[5] DING Zi-ang, LE Cao-wei, WU Ling-ling and FU Ming-lei. PM2.5 Concentration Prediction Method Based on CEEMD-Pearson and Deep LSTM Hybrid Model [J]. Computer Science, 2020, 47(6A): 444-449.
[6] LIN Xu-dan, BAO Shi-Jian, ZHAO Li-xin and ZHAO Chen-lin. Design and Performance Analysis of Automotive Supply Chain System Based on Hyperledger Fabric [J]. Computer Science, 2020, 47(6A): 546-551.
[7] KE Yu-Jing, JING Mao-hua and ZHENG Han-yin. Application Research of Blockchain Technology in Trust Industry [J]. Computer Science, 2020, 47(6A): 591-595.
[8] ZHANG Qi-ming, LU Jian-hua, LI Shou-zhi and XU Jian-dong. Building Innovative Enterprise Customer Service Technology Platform Based on Blockchain [J]. Computer Science, 2020, 47(6A): 639-642.
[9] YE Shao-jie, WANG Xiao-yi, XU Cai-chao, SUN Jian-ling. BitXHub:Side-relay Chain Based Heterogeneous Blockchain Interoperable Platform [J]. Computer Science, 2020, 47(6): 294-302.
[10] SHANG Jun-yuan, YANG Le-han, HE Kun. Analyzing Latent Representation of Deep Neural Networks Based on Feature Visualization [J]. Computer Science, 2020, 47(5): 190-197.
[11] XIE Ying-ying, SHI Jian, HUANG Shuo-kang, LEI Kai. Survey on Internet of Things Based on Named Data Networking Facing 5G [J]. Computer Science, 2020, 47(4): 217-225.
[12] WANG Hui, LIU Yu-xiang, CAO Shun-xiang, ZHOU Ming-ming. Medical Data Storage Mechanism Integrating Blockchain Technology [J]. Computer Science, 2020, 47(4): 285-291.
[13] FENG Tao, JIAO Ying, FANG Jun-li, TIAN Ye. Medical Health Data Security Model Based on Alliance Blockchain [J]. Computer Science, 2020, 47(4): 305-311.
[14] TANG Guo-qiang,GAO Da-qi,RUAN Tong,YE Qi,WANG Qi. Clinical Electronic Medical Record Named Entity Recognition Incorporating Language Model and Attention Mechanism [J]. Computer Science, 2020, 47(3): 211-216.
[15] PAN Ji-fei,HUANG De-cai. Blockchain Dynamic Sharding Model Based on Jump Hash and Asynchronous Consensus Group [J]. Computer Science, 2020, 47(3): 273-280.
Full text



[1] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[2] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .
[3] ZHAO Li-bo, LIU Qi, FU Fang-ling and HE Ling. Automatic Detection of Hypernasality Grades Based on Discrete Wavelet Transformation and Cepstrum Analysis[J]. Computer Science, 2018, 45(4): 278 -284 .
[4] LI Shan and RAO Wen-bi. Video-based Detection of Human Motion Area in Mine[J]. Computer Science, 2018, 45(4): 291 -295 .
[5] XIANG Ying-zhuo, TAN Ju-xian, HAN Jie-si, SHI Hao. Survey of Graph Matching Algorithms[J]. Computer Science, 2018, 45(6): 27 -31 .
[6] HU Ya-peng, DING Wei-long, WANG Gui-ling. Monitoring and Dispatching Service for Heterogeneous Big Data Computing Frameworks[J]. Computer Science, 2018, 45(6): 67 -71 .
[7] GUO Ying-ying, ZHANG Li-ping, LI Song. Group Nearest Neighbor Query Method of Line Segment in Obstacle Space[J]. Computer Science, 2018, 45(6): 172 -175 .
[8] HAN Zhao, MIAO Duo-qian, REN Fu-ji. Rough Set Based Knowledge Predicate Analysis of Chinese Knowledge Based Question Answering[J]. Computer Science, 2018, 45(6): 183 -186 .
[9] LAI Wen-xing, DENG Zhong-min. Improved NSGA2 Algorithm Based on Dominant Strength[J]. Computer Science, 2018, 45(6): 187 -192 .
[10] WANG Jing-song, CAI Zhao-hui, LI Yong-kai and LIU Shu-bo. Collaborative Filtering Recommendation Algorithm Based on Difference and Correlation of Users’ Ratings[J]. Computer Science, 2018, 45(5): 190 -195 .