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Ethereum fraud behavior detection based on graph neural networks

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Abstract

Since Bitcoin was first conceived in 2008, blockchain technology has attracted a large amount of researchers’ attention. At the same time, it has also facilitated a variety of cybercrimes. For example, Ethereum frauds, due to the potential for huge profits, occur frequently and pose a serious threat to the financial security of the Ethereum network. To create healthy financial environments, methods for automatically detecting and identifying Ethereum frauds are urgently needed in Ethereum system governance. To this end, this paper proposes a new framework to detect fraudulent transactions in Ethereum by mining Ethereum transaction records. Specifically, we obtain Ethereum addresses with fraud/legitimate labels through Web crawlers and then construct a transaction network according to the public transaction ledger. Then, a transaction behavior-based network embedding algorithm is proposed to extract node features for subsequent fraudulent transaction identification. Finally, we adopt the Graph Convolutional Neural Network model (GCN) to classify addresses into legal and fraudulent addresses. The experimental results show that the fraudulent transaction detection system can achieve an accuracy of 96% on fraud/legitimate record classification, which proves the effectiveness of the framework in the detection of Ethereum fraudulent transactions.

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References

  1. Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system. Decentralized Business Review, 21260

  2. Yaga D, Mell P, Roby N, Scarfone K (2019) Blockchain technology overview. arXiv preprint arXiv:1906.11078

  3. Beck R, Czepluch JS, Lollike N, Malone S (2016) Blockchain–the gateway to trust-free cryptographic transactions. In: Twenty-Fourth European conference on information systems (ECIS), İstanbul, Turkey. Springer Publishing Company, pp 1–14

  4. Singh S, Singh N (2016) Blockchain: future of financial and cyber security. In: 2016 2nd international conference on contemporary computing and informatics (IC3I). IEEE, pp 463–467

  5. Liu C, Xiao Y, Javangula V, Hu Q, Wang S, Cheng X (2018) Normachain: a blockchain-based normalized autonomous transaction settlement system for IoT-based e-commerce. IEEE Internet Things J 6(3):4680–4693

    Article  Google Scholar 

  6. Liang W, Zhang D, Lei X, Tang M, Li K-C, Zomaya AY (2020) Circuit copyright blockchain: blockchain-based homomorphic encryption for IP circuit protection. IEEE Trans Emerg Top Comput 9(3):1410–1420

    Article  Google Scholar 

  7. Pham HL, Tran TH, Nakashima Y (2019) Practical anti-counterfeit medicine management system based on blockchain technology. In: 2019 4th technology innovation management and engineering science international conference (TIMES-iCON), IEEE, pp 1–5

  8. Bayatmakou BS (2018) Money laundering and black markets. In: Human-computer interaction and cybersecurity handbook. CRC Press, pp 157–174

  9. Wu J, Yuan Q, Lin D, You W, Chen W, Chen C, Zheng Z (2020) Who are the phishers? Phishing scam detection on ethereum via network embedding. IEEE Trans Syst Man Cyber Syst 52(2):1156–1166

    Article  Google Scholar 

  10. Conti M, Kumar ES, Lal C, Ruj S (2018) A survey on security and privacy issues of bitcoin. IEEE Commun Surv Tutor 20(4):3416–3452

    Article  Google Scholar 

  11. Unknown: Scammers “exploit Chaos” to Steal \$1 Million on Ethereum Merge. (2022, Nov 4). https://0xzx.com/2022110402042812029.html

  12. Castellanos JAF, Coll-Mayor D, Notholt JA (2017) Cryptocurrency as guarantees of origin: simulating a green certificate market with the ethereum blockchain. In: 2017 IEEE international conference on smart energy grid engineering (SEGE). IEEE, pp 367–372

  13. Khonji M, Iraqi Y, Jones A (2013) Phishing detection: a literature survey. IEEE Commun Surv Tutor 15(4):2091–2121

    Article  Google Scholar 

  14. Ganesan R et al. (2014) Data security in cloud architecture based on diffie hellman and elliptical curve cryptography. Cryptology ePrint Archive

  15. Chao X, Kou G, Peng Y, Alsaadi FE (2019) Behavior monitoring methods for trade-based money laundering integrating macro and micro prudential regulation: a case from china. Technol Econ Dev Econ 25(6):1081–1096

    Article  Google Scholar 

  16. Spagnuolo M, Maggi F, Zanero S (2014) Bitiodine: extracting intelligence from the bitcoin network. In: International conference on financial cryptography and data security. Springer, pp 457–468.

  17. Fleder M, Kester MS, Pillai S (2015) Bitcoin transaction graph analysis. arXiv preprint arXiv:1502.01657

  18. Pham T, Lee S (2016) Anomaly detection in bitcoin network using unsupervised learning methods. arXiv preprint arXiv:1611.03941

  19. Monamo P, Marivate V, Twala B (2016) Unsupervised learning for robust bitcoin fraud detection. In: 2016 Information security for south Africa (ISSA). IEEE, pp 129–134

  20. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: large-scale information network embedding. In: 24th International conference on world wide web, pp 1067–1077

  21. Grover A, Leskovec J (2016) node2vec: scalable feature learning for networks. In: 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 855–864

  22. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 701–710

  23. Qiu J, Dong Y, Ma H, Li J, Wang K, Tang J (2018) Network embedding as matrix factorization: unifying deepwalk, line, pte, and node2vec. In: Eleventh ACM international conference on web search and data mining, pp 459–467

  24. Kaur A, Sharma P, Verma A (2014) A appraisal paper on breadth-first search, depth-first search and red black tree. Int J Sci Res Publ 4(3):2–4

    Google Scholar 

  25. Wang Y, Dong L, Jiang X, Ma X, Li Y, Zhang H (2021) Kg2vec: a node2vec-based vectorization model for knowledge graph. PLoS ONE 16(3):0248552

    Article  Google Scholar 

  26. Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260

    Article  MathSciNet  MATH  Google Scholar 

  27. Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80

    Article  Google Scholar 

  28. Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inform Proc Syst, 29

  29. Veličković P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903

  30. Weber M, Domeniconi G, Chen J, Weidele DKI, Bellei C, Robinson T, Leiserson CE (2019) Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591

  31. Patel V, Pan L, Rajasegarar S (2020) Graph deep learning based anomaly detection in ethereum blockchain network. In: International conference on network and system security. Springer, pp 132–148

  32. Wood G et al (2014) Ethereum: a secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper 151(2014):1–32

    Google Scholar 

  33. Buterin V et al. (2014) A next-generation smart contract and decentralized application platform. White paper 3(37)

  34. Chen W, Guo X, Chen Z, Zheng Z, Lu Y (2020) Phishing scam detection on ethereum: towards financial security for blockchain ecosystem. In: IJCAI, pp 4506–4512

  35. Bartoletti M, Carta S, Cimoli T, Saia R (2020) Dissecting ponzi schemes on ethereum: identification, analysis, and impact. Futur Gener Comput Syst 102:259–277

    Article  Google Scholar 

  36. Bistarelli S, Mazzante G, Micheletti M, Mostarda L, Sestili D, Tiezzi F (2020) Ethereum smart contracts: analysis and statistics of their source code and opcodes. Internet Things 11:100198

    Article  Google Scholar 

  37. Chen L, Peng J, Liu Y, Li J, Xie F, Zheng Z (2021) Phishing scams detection in ethereum transaction network. ACM Trans Internet Techn 21(1):10–11016. https://doi.org/10.1145/3398071

    Article  Google Scholar 

  38. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  39. Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inform Proc Syst 30

  40. Wang MY (2019) Deep graph library: towards efficient and scalable deep learning on graphs. In: ICLR Workshop on representation learning on graphs and manifolds

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (61972105), Joint Research Fund of Guangzhou and University (202201020181), and the Guangdong Province Key Research and Development Plan (2019B010137003), and in part by the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2019).

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Authors and Affiliations

  1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, Guangdong, China

    Runnan Tan, Qingfeng Tan & Peng Zhang

  2. Big Data Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Qin Zhang

  3. Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Yushun Xie

  4. Zhejiang University, Hangzhou, Zhejiang, China

    Zhao Li

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  1. Runnan Tan
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  2. Qingfeng Tan
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  4. Peng Zhang
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  5. Yushun Xie
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Correspondence to Qingfeng Tan or Peng Zhang.

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Tan, R., Tan, Q., Zhang, Q. et al. Ethereum fraud behavior detection based on graph neural networks. Computing 105, 2143–2170 (2023). https://doi.org/10.1007/s00607-023-01177-7

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