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Detection of Smart Ponzi Schemes Using Opcode

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Blockchain and Trustworthy Systems (BlockSys 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1267))

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Abstract

Blockchain is becoming an important infrastructure of the next generation of information technology. But now, the fraud on blockchain is serious which has affected the development of blockchain ecology. Smart Ponzi scheme which realized by smart contract is a new type of Ponzi scheme and running on Ethereum. It would cause more serious damage to society in less time than other Ponzi schemes. Timely and comprehensive detection of all smart Ponzi schemes is the key to constructed an automatic detection model of smart Ponzi scheme. A model that effectively detect smart Ponzi scheme in its full lifecycle is proposed in this paper. The model only uses features based on operation codes (i.e., opcodes) of smart contract on Ethereum. The systematic modeling strategy realizes the efficient automatic detection model of smart Ponzi scheme step by step. Precision, Recall and F1-score of the model are 0.98, 0.93 and 0.95 respectively by experiments. Smart Ponzi schemes hidden on Ethereum are detected effectively by the model. More importantly, the performance of model is guaranteed at any moment in the lifecycle, even at the birth of a smart Ponzi scheme.

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References

  1. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system [OL]. https://bitcoin.org/bitcoin.pdf

  2. CoinDesk. (2017). Understanding Ethereum-blockchain Research Report. www.coindesk.com/research/understandingethereum-report/

  3. Swan, M.: Blockchain: Blueprint for a New Economy. O’Reilly Media, Inc., Sebastopol (2015)

    Google Scholar 

  4. Chen, W., Zheng, Z.: Blockchain data analysis: a review of status, trends and challenges. J. Comput. Res. Dev. 55(9), 1853–1870 (2018)

    Google Scholar 

  5. Yli-Huumo, J., Ko, D., Choi, S., Park, S., Smolander, K.: Where is current research on blockchain technology?-a systematic review. PLoS ONE 11(10), e0163477 (2016)

    Article  Google Scholar 

  6. Buterin, V.: A next-generation smart contract and decentralized application platform (2014)

    Google Scholar 

  7. Gandal, N., Hamrick, J.T., Moore, T., et al.: Price manipulation in the Bitcoin ecosystem. J. Monetary Econ. 95, 86–96 (2018)

    Article  Google Scholar 

  8. Brito, J., Castillo, A.: Bitcoin: a primer for policymakers. Mercatus Center at George Mason University (2013)

    Google Scholar 

  9. Slattery, T.: Taking a bit out of crime: Bitcoin and cross-border tax evasion. Brook. J. Int. L. 39, 829 (2014)

    Google Scholar 

  10. Atzei, N., Bartoletti, M., Cimoli, T.: A survey of attacks on ethereum smart contracts (SoK). In: Maffei, M., Ryan, M. (eds.) POST 2017. LNCS, vol. 10204, pp. 164–186. Springer, Heidelberg (2017). https://doi.org/10.1007/978-3-662-54455-6_8

    Chapter  Google Scholar 

  11. Chen, W., Wu, J., Zheng, Z., Chen, C., Zhou Y.: Evidence from Mining the Mt. Gox Transaction Network, Market Manipulation of Bitcoin (2019)

    Google Scholar 

  12. Moser, M., Bohme, R., Breuker, D.: An inquiry into money laundering tools in the Bitcoin ecosystem. Proceedings of the APWG Symposium on Electronic Crime Research. San Francisco, USA, pp. 1–14 (2013)

    Google Scholar 

  13. Tasca, P., Hayes, A., Liu, S.: The evolution of the bitcoin economy: extracting and analyzing the network of payment relationships. J. Risk Finan. 19(2), 94–126 (2018)

    Article  Google Scholar 

  14. Christin, N.: Traveling the silk road: a measurement analysis of a large anonymous online marketplace. In: Proceedings of the International World Wide Web Conference. Rio de Janeiro, Brazil, pp. 213–224 (2013)

    Google Scholar 

  15. Vasek, M., Moore, T.: There’s no free lunch, even using bitcoin: tracking the popularity and profits of virtual currency scams. In: Böhme, R., Okamoto, T. (eds.) FC 2015. LNCS, vol. 8975, pp. 44–61. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-47854-7_4

    Chapter  Google Scholar 

  16. Karame, G.O., Androulaki, E., Roeschlin, M., et al.: Misbehavior in bitcoin: a study of double-spending and accountability. ACM Trans. Inf. Syst. Secur. (TISSEC) 18(1), 2 (2015)

    Article  Google Scholar 

  17. Eyal, I., Sirer, E.G.: Majority is not enough: Bitcoin mining is vulnerable. Commun. ACM 61(7), 95–102 (2018)

    Article  Google Scholar 

  18. Bhargavan, K., Delignat-Lavaud, A., Fournet, C., et al.: Formal verification of smart contracts: Short paper. In: Proceedings of the 2016 ACM Workshop on Programming Languages and Analysis for Security, pp. 91–96. ACM (2016)

    Google Scholar 

  19. Moore, T., Han, J., Clayton, R.: The postmodern ponzi scheme: empirical analysis of high-yield investment programs. In: Keromytis, A.D. (ed.) FC 2012. LNCS, vol. 7397, pp. 41–56. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32946-3_4

  20. Szabo, N.: Smart contracts: building blocks for digital markets, September 1996. http://www.fon.hum.uva.nl/. Accessed 1 Oct 2017

  21. Bartoletti, M., Pes, B., Serusi, S.: Data mining for detecting bitcoin Ponzi schemes (2018). http://arxiv.org/abs/1803.00646

  22. Toyoda, K., Mathiopoulos, P.T., Ohtsuki, T.: A novel methodology for hyip operators’ Bitcoin addresses identification. IEEE Access 7, 74835–74848 (2019). https://doi.org/10.1109/ACCESS.2019.2921087

    Article  Google Scholar 

  23. Chen, W., Zheng, Z., Ngai, E., et al.: Exploiting Blockchain data to detect smart ponzi schemes on ethereum. IEEE Access 1, 37575–37586 (2019)

    Article  Google Scholar 

  24. Chen, W., Zheng, Z., Cui, J., et al.: Detecting ponzi schemes on ethereum: towards healthier blockchain technology. In: The 2018 World Wide Web Conference (2018)

    Google Scholar 

  25. Bartoletti, M., Carta, S., Cimoli, T., Saia, R.: Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact (2017). https://arxiv.org/ abs/1703.03779

  26. Smith, F.: Madoff ponzi scheme exposes the myth of the sophisticated investor. U. Balt. L. Rev. 40, 215 (2010)

    Google Scholar 

  27. Davis, J., et al.: The relationship between precision-recall and ROC curves. In: ICML (2006)

    Google Scholar 

  28. Moore, T., Han, J., Clayton, R.: The postmodern ponzi scheme: empirical analysis of high-yield investment programs. In: Keromytis, A.D. (ed.) FC 2012. LNCS, vol. 7397, pp. 41–56. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32946-3_4

    Chapter  Google Scholar 

  29. Yujian, L., Bo, L.: A normalized Levenshtein distance metric. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1091–1095 (2007)

    Article  Google Scholar 

  30. S. C. A. Chamber of Digital Commerce, “Smart contracts - Is the law ready,” no. September 2018

    Google Scholar 

  31. Abou-Assaleh, T., Keselj, V., Sweidan, R.: N-gram based detection of new malicious code. In: Proceedings of the 28th Annual International Computer Software and Applications Conference, IEEE Computer Society, 41–42 (2004)

    Google Scholar 

  32. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18, 613–620 (1975)

    Article  Google Scholar 

  33. Kleinbaum, D.G., Klein, M.: Logistic Regression. SBH. Springer, New York (2010). https://doi.org/10.1007/978-1-4419-1742-3

    Book  MATH  Google Scholar 

  34. Quinlan, R.J.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  35. Hearst, M.A., Dumais, S.T., Osman, E., et al.: Support vector machines. IEEE Intell. Syst. 13(4), 18–28 (1998)

    Article  Google Scholar 

  36. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  Google Scholar 

  37. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)

    Article  Google Scholar 

  38. Natekin, A., Knoll, A., et al.: Gradient boosting machines. a tutorial. Front. Neurorobotics, 7, 21 (2013). https://doi.org/10.3389/fnbot.2013.00021

  39. Chen, T., Guestrin, C., et al.: XGBoost: A Scalable Tree Boosting System, pp 785–794 (2016). https://doi.org/10.1145/2939672.2939785

  40. Qi, M.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree (2018)

    Google Scholar 

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Acknowledgments

This work was supported in part by the Guangdong Provincial Key R&D Program under Grant No. 2020B010166005, in part by the Characteristic innovation Program in Universities and Colleges of Guangdong Province, in part by the Youth Innovation Talent Program in Universities and Colleges of Guangdong Province(2018WQNCX301), in part by the General Research Program of South China Business College, Guangdong University of Foreign Studies(19-025B).

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Authors and Affiliations

  1. Foshan Polytechnic, Foshan, 528137, GuangDong, China

    Jianxi Peng

  2. South China Business College, Guangdong University of Foreign Studies, Guangzhou, 510006, China

    Guijiao Xiao

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  1. Jianxi Peng
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  2. Guijiao Xiao
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Correspondence to Jianxi Peng .

Editor information

Editors and Affiliations

  1. Sun Yat-sen University, Guangzhou, China

    Zibin Zheng

  2. Macau University of Science and Technology, Macau, China

    Hong-Ning Dai

  3. Kunming University of Science and Technology, Kunming, China

    Xiaodong Fu

  4. Dali University, Dali, China

    Benhui Chen

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Peng, J., Xiao, G. (2020). Detection of Smart Ponzi Schemes Using Opcode. In: Zheng, Z., Dai, HN., Fu, X., Chen, B. (eds) Blockchain and Trustworthy Systems. BlockSys 2020. Communications in Computer and Information Science, vol 1267. Springer, Singapore. https://doi.org/10.1007/978-981-15-9213-3_15

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  • DOI: https://doi.org/10.1007/978-981-15-9213-3_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-9212-6

  • Online ISBN: 978-981-15-9213-3

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