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It’s All Connected: Detecting Phishing Transaction Records on Ethereum Using Link Prediction

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Hybrid Intelligent Systems (HIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 647))

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Abstract

Digital currencies are increasingly being used on platforms for virtual transactions, such as Ethereum, owing to new financial innovations. As these platforms are anonymous and easy to use, they are perfect places for phishing scams to grow. Unlike traditional phishing detection approaches that aim to distinguish phishing websites and emails using their HTML content and URLs, phishing attacks on Ethereum focus on detecting phishing addresses by analyzing the transaction relationships on the virtual transaction platform. This study proposes a link prediction framework for detecting phishing transactions on the Ethereum platform using 12 local network-based features extracted from the Ether receiving and initiating addresses. The framework was trained and tested on over 280,000 verified phishing and legitimate transaction records. Experimental results indicate that the proposed framework with a LightGBM classifier provides a high recall of 89% and an AUC score of 93%.

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Notes

  1. 1.

    http://xblock.pro/#/dataset/6.

References

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

    Google Scholar 

  2. Gutierrez, C.N., Kim, T., Della Corte, R., Avery, J., Goldwasser, D., Cinque, M., Bagchi, S.: Learning from the ones that got away: detecting new forms of phishing attacks. IEEE Trans. Dependable Secur. Comput. 15(6), 988–1001 (2018)

    Article  Google Scholar 

  3. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y.: Lightgbm: a highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30 (2017)

    Google Scholar 

  4. Lin, D., Wu, J., Xuan, Q., Chi, K.T.: Ethereum transaction tracking: inferring evolution of transaction networks via link prediction. Phys. A: Stat. Mech. Its Appl. 600, 127504 (2022)

    Article  MathSciNet  Google Scholar 

  5. Liu, X., Tang, Z., Li, P., Guo, S., Fan, X., Zhang, J.: A graph learning based approach for identity inference in dapp platform blockchain. IEEE Trans. Emerg. Top. Comput. (2020)

    Google Scholar 

  6. Minastireanu, E.A., Mesnita, G.: Light gbm machine learning algorithm to online click fraud detection. J. Inform. Assur. Cybersecur (2019)

    Google Scholar 

  7. Opara, C., Chen, Y., et al.: Look before you leap: detecting phishing web pages by exploiting raw url and html characteristics. arXiv:2011.04412 (2020)

  8. Opara, C., Wei, B., Chen, Y.: Htmlphish: enabling phishing web page detection by applying deep learning techniques on html analysis. In: 2020 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. IEEE (2020)

    Google Scholar 

  9. Pannell, D.J.: Sensitivity analysis of normative economic models: theoretical framework and practical strategies. Agric. Econ. 16(2), 139–152 (1997)

    Article  Google Scholar 

  10. Wang, J., Chen, P., Yu, S., Xuan, Q.: Tsgn: Transaction subgraph networks for identifying ethereum phishing accounts. In: International Conference on Blockchain and Trustworthy Systems, pp. 187–200. Springer (2021)

    Google Scholar 

  11. Wood, G., et al.: Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj. Yellow Pap. 151(2014), 1–32 (2014)

    Google Scholar 

  12. Wu, J., Yuan, Q., Lin, D., You, W., Chen, W., Chen, C., Zheng, Z.: Who are the phishers? Phishing scam detection on ethereum via network embedding. IEEE Trans. Syst. Man Cybern.: Syst. (2020)

    Google Scholar 

  13. Yuan, Z., Yuan, Q., Wu, J.: Phishing detection on ethereum via learning representation of transaction subgraphs. In: International Conference on Blockchain and Trustworthy Systems, pp. 178–191. Springer (2020)

    Google Scholar 

  14. Zhuang, Y., Liu, Z., Qian, P., Liu, Q., Wang, X., He, Q.: Smart contract vulnerability detection using graph neural network. In: IJCAI, pp. 3283–3290 (2020)

    Google Scholar 

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Correspondence to Chidimma Opara .

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Opara, C., Chen, Y., Wei, B. (2023). It’s All Connected: Detecting Phishing Transaction Records on Ethereum Using Link Prediction. In: Abraham, A., Hong, TP., Kotecha, K., Ma, K., Manghirmalani Mishra, P., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2022. Lecture Notes in Networks and Systems, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-031-27409-1_107

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