Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Who is gambling? Finding cryptocurrency gamblers using multi-modal retrieval methods

  • Regular Paper
  • Published:
International Journal of Multimedia Information Retrieval Aims and scope Submit manuscript

Abstract

With the popularity of cryptocurrencies and the remarkable development of blockchain technology, decentralized applications emerged as a revolutionary force for the Internet. Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts. Compared with conventional gambling platforms, decentralized gambling has transparent rules and a low participation threshold, attracting a substantial number of gamblers. In order to discover gambling behaviors and identify the contracts and addresses involved in gambling, we propose a tool termed ETHGamDet. The tool is able to automatically detect the smart contracts and addresses involved in gambling by scrutinizing the smart contract code and address transaction records. Interestingly, we present a novel LightGBM model with memory components, which possesses the ability to learn from its own misclassifications. As a side contribution, we construct and release a large-scale gambling dataset at https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset to facilitate future research in this field. Empirically, ETHGamDet achieves a F1-score of 0.72 and 0.89 in address classification and contract classification respectively, and offers novel and interesting insights.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

The datasets analyzed during the current study are available in the GitHub repository [25], https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset.

References

  1. (2022) Degens - the ethereum betting exchange. Website, https://degens.com/

  2. (2022) Dicether. Website, https://dicether.com/

  3. Akcora CG, Li Y, Gel YR, et al (2020) Bitcoinheist: topological data analysis for ransomware prediction on the bitcoin blockchain. In: Proceedings of the twenty-ninth international joint conference on artificial intelligence

  4. Albert E, Gordillo P, Livshits B, et al (2018) Ethir: a framework for high-level analysis of ethereum bytecode. In: International symposium on automated technology for verification and analysis, Springer, pp 513–520

  5. Ante L, Fiedler I, Strehle E (2021) The impact of transparent money flows: Effects of stablecoin transfers on the returns and trading volume of bitcoin. Technological Forecasting and Social Change 170(120):851

    Google Scholar 

  6. Atzei N, Bartoletti M, Cimoli T (2017) A survey of attacks on ethereum smart contracts (sok). In: International conference on principles of security and trust, Springer, pp 164–186

  7. Ayed AB (2017) A conceptual secure blockchain-based electronic voting system. Int J Network Sec Appl 9(3):01–09

    Google Scholar 

  8. Bhargavan K, Delignat-Lavaud A, Fournet C, et al (2016) Formal verification of smart contracts: Short paper. In: Proceedings of the 2016 ACM workshop on programming languages and analysis for security, pp 91–96

  9. Brent L, Jurisevic A, Kong M, et al (2018) Vandal: a scalable security analysis framework for smart contracts. arXiv preprint arXiv:1809.03981

  10. Broadhurst R, Lord D, Maxim D, et al (2018) Malware trends on ‘darknet’crypto-markets: research review. Available at SSRN 3226758

  11. Campbell-Verduyn M (2018) Bitcoin, crypto-coins, and global anti-money laundering governance. Crime, Law and Social Change 69(2):283–305

    Article  Google Scholar 

  12. Chen T, He T, Benesty M et al (2015) Xgboost: extreme gradient boosting. R package version 04-2 1(4):1–4

  13. Chen T, Li X, Luo X, et al (2017) Under-optimized smart contracts devour your money. In: 2017 IEEE 24th international conference on software analysis, evolution and reengineering (SANER), IEEE, pp 442–446

  14. Chen W, Zheng Z, Cui J, et al (2018) Detecting ponzi schemes on ethereum: towards healthier blockchain technology. In: Proceedings of the 2018 world wide web conference, pp 1409–1418

  15. Chen W, Wu J, Zheng Z, et al (2019) Market manipulation of bitcoin: Evidence from mining the mt. gox transaction network. In: IEEE INFOCOM 2019-IEEE conference on computer communications, IEEE, pp 964–972

  16. Chirtoaca D, Ellul J, Azzopardi G (2020) A framework for creating deployable smart contracts for non-fungible tokens on the ethereum blockchain. In: 2020 IEEE international conference on decentralized applications and infrastructures (DAPPS), IEEE, pp 100–105

  17. Er-Rajy L, El Kiram My A, El Ghazouani M et al (2017) Blockchain: Bitcoin wallet cryptography security, challenges and countermeasures. Journal of Internet Banking and Commerce 22(3):1–29

    Google Scholar 

  18. Feng Q, He D, Zeadally S et al (2019) A survey on privacy protection in blockchain system. Journal of Network and Computer Applications 126:45–58

    Article  Google Scholar 

  19. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat pp 1189–1232

  20. Fu Y, Ren M, Ma F, et al (2019) Evmfuzzer: detect evm vulnerabilities via fuzz testing. In: Proceedings of the 2019 27th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering, pp 1110–1114

  21. Grech N, Kong M, Jurisevic A et al (2018) Madmax: surviving out-of-gas conditions in ethereum smart contracts. In: Proceedings of the ACM on programming languages 2(OOPSLA):1–27

  22. Grech N, Brent L, Scholz B, et al (2019) Gigahorse: thorough, declarative decompilation of smart contracts. In: 2019 IEEE/ACM 41st international conference on software engineering (ICSE), IEEE, pp 1176–1186

  23. Guo Y, Liang C (2016) Blockchain application and outlook in the banking industry. Financial innovation 2(1):1–12

    Article  Google Scholar 

  24. Hildenbrandt E, Saxena M, Rodrigues N, et al (2018) Kevm: a complete formal semantics of the ethereum virtual machine. In: 2018 IEEE 31st computer security foundations symposium (CSF), IEEE, pp 204–217

  25. Huang Z (2022) Bitcoin gambling dataset. Website, https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset

  26. Kalra S, Goel S, Dhawan M, et al (2018) Zeus: analyzing safety of smart contracts. In: Ndss, pp 1–12

  27. Ke G, Meng Q, Finley T, et al (2017) Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inf Process Syst 30

  28. Lee C, Maharjan S, Ko K, et al (2019) Toward detecting illegal transactions on bitcoin using machine-learning methods. In: International conference on blockchain and trustworthy systems, Springer, pp 520–533

  29. Li P, Xu H, Ma T (2021) An efficient identity tracing scheme for blockchain-based systems. Information Sciences 561:130–140

    Article  MathSciNet  Google Scholar 

  30. Liu J, Liu Z (2019) A survey on security verification of blockchain smart contracts. IEEE Access 7:77894–77904

    Article  Google Scholar 

  31. Liu Z, Qian P, Wang X, et al (2021) Smart contract vulnerability detection: from pure neural network to interpretable graph feature and expert pattern fusion. arXiv preprint arXiv:2106.09282

  32. Liu Z, Qian P, Wang X, et al (2021) Combining graph neural networks with expert knowledge for smart contract vulnerability detection. IEEE Trans Knowl Data Eng

  33. Luu L, Chu DH, Olickel H, et al (2016) Making smart contracts smarter. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pp 254–269

  34. Macrinici D, Cartofeanu C, Gao S (2018) Smart contract applications within blockchain technology: A systematic mapping study. Telematics and Informatics 35(8):2337–2354

    Article  Google Scholar 

  35. Mehar MI, Shier CL, Giambattista A et al (2019) Understanding a revolutionary and flawed grand experiment in blockchain: the dao attack. Journal of Cases on Information Technology (JCIT) 21(1):19–32

    Article  Google Scholar 

  36. Miller JJ (2013) Graph database applications and concepts with neo4j. In: Proceedings of the southern association for information systems conference, Atlanta, GA, USA

  37. Mohanta BK, Panda SS, Jena D (2018) An overview of smart contract and use cases in blockchain technology. In: 2018 9th international conference on computing, communication and networking technologies (ICCCNT), IEEE, pp 1–4

  38. Morishima S (2021) Scalable anomaly detection in blockchain using graphics processing unit. Computers & Electrical Engineering 92(107):087

    Google Scholar 

  39. Norta A (2016) Designing a smart-contract application layer for transacting decentralized autonomous organizations. In: International conference on advances in computing and data sciences, Springer, pp 595–604

  40. Qian P, Liu Z, Wang X, et al (2019) Digital resource rights confirmation and infringement tracking based on smart contracts. In: 2019 IEEE 6th international conference on cloud computing and intelligence systems (CCIS), IEEE, pp 62–67

  41. Scholten OJ, Zendle D, Walker JA (2020) Inside the decentralised casino: A longitudinal study of actual cryptocurrency gambling transactions. PloS one 15(10):e0240,693

  42. Suiche M (2017) Porosity: a decompiler for blockchain-based smart contracts bytecode. DEF con 25(11)

  43. Szabo N, et al (1994) Smart contracts

  44. Team E (2017) Etherscan: The ethereum block explorer. https://etherscan.io

  45. Tsankov P, Dan A, Drachsler-Cohen D, et al (2018) Securify: practical security analysis of smart contracts. In: Proceedings of the 2018 ACM SIGSAC conference on computer and communications security, pp 67–82

  46. Victor F, Lüders BK (2019) Measuring ethereum-based erc20 token networks. In: International conference on financial cryptography and data security, Springer, pp 113–129

  47. Warren W, Bandeali A (2017) 0x: An open protocol for decentralized exchange on the ethereum blockchain. https://githubcom/0xProject/whitepaper, pp 04–18

  48. Webber J (2012) A programmatic introduction to neo4j. In: Proceedings of the 3rd annual conference on Systems, programming, and applications: software for humanity, pp 217–218

  49. Wood G (2014) Ethereum yellow paper. Internet: https://githubcom/ethereum/yellowpaper, [Oct 30, 2018] p 30

  50. Wu J, Yuan Q, Lin D, et al (2020) Who are the phishers? phishing scam detection on ethereum via network embedding. IEEE Trans Syst Man Cybern Syst

  51. Yan C, Zhang C, Lu Z et al (2022) Blockchain abnormal behavior awareness methods: a survey. Cybersecurity 5(1):1–27

    Article  Google Scholar 

  52. Zheng Z, Xie S, Dai HN et al (2018) Blockchain challenges and opportunities: A survey. Int J Web Grid Serv 14(4):352–375

    Article  Google Scholar 

  53. Zhou Y, Kumar D, Bakshi S, et al (2018) Erays: reverse engineering ethereum’s opaque smart contracts. In: 27th USENIX security symposium (USENIX Security 18), pp 1371–1385

  54. Zhuang Y, Liu Z, Qian P, et al (2020) Smart contract vulnerability detection using graph neural network. In: IJCAI, pp 3283–3290

  55. Zichichi M, Contu M, Ferretti S, et al (2019) Likestarter: a smart-contract based social dao for crowdfunding. In: IEEE INFOCOM 2019-IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, pp 313–318

Download references

Acknowledgements

This work was supported in part by the National Key R &D Program of China (2021YFB2700500); in part by the National Natural Science Foundation of China (No. 61902348); and in part by the Key R &D Program of Zhejiang Province (No. 2021C01104).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhenguang Liu.

Ethics declarations

Conflict of interest

All the authors have checked the manuscript and have agreed to the submission in International Journal of Multimedia Information Retrieval. There is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Z., Liu, Z., Chen, J. et al. Who is gambling? Finding cryptocurrency gamblers using multi-modal retrieval methods. Int J Multimed Info Retr 11, 539–551 (2022). https://doi.org/10.1007/s13735-022-00264-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13735-022-00264-3

Keywords

Navigation