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Graph Deep Learning Based Anomaly Detection in Ethereum Blockchain Network

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Network and System Security (NSS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12570))

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Abstract

Ethereum is one of the largest blockchain networks in the world. Its feature of smart contracts is unique among the other crypto-currencies and gained wider attention. However, smart contracts are vulnerable to attacks and financial fraud within the network. Identifying anomalies in this massive network is challenging because of anonymity. Using traditional machine learning-based techniques, such as One-Class Support Vector Machine and Isolation Forest are ineffective in Identifying anomalies in the Ethereum transactions because of its limitations in terms of capturing the internode or account relationship information in the transactions. Ethereum transactions can be effectively represented using an attributed graph with nodes and edges capturing the inter-dependencies. Hence, in this paper, we propose to use a One-Class Graph Neural Network-based anomaly detection framework for detecting anomalies in the Ethereum blockchain network. Empirical evaluation demonstrates that the proposed method is able to achieve higher anomaly detection accuracy than traditional non-graph based machine learning algorithms.

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Notes

  1. 1.

    https://bit.ly/344VBEp.

  2. 2.

    https://github.com/vatsalpatels/Graph-DL-Based-Anomaly-Detection-in-Ethereum.git.

References

  1. Bai, Q., Zhang, C., Xu, Y., Chen, X., Wang, X.: Evolution of ethereum: a temporal graph perspective. arXiv preprint arXiv:2001.05251 (2020)

  2. Chen, T., et al.: Understanding ethereum via graph analysis. In: Proceedings of the IEEE Conference on Computer Communications (INFOCOM’ 2018), pp. 1484–1492 (2018)

    Google Scholar 

  3. Cheng, Z., et al.: Towards a first step to understand the cryptocurrency stealing attack on ethereum. In: Proceedings of the 22nd International Symposium on Research in Attacks, Intrusions and Defenses (RAID’ 2019), pp. 47–60 (2019)

    Google Scholar 

  4. Galke, L., Vagliano, I., Scherp, A.: Incremental training of graph neural networks on temporal graphs under distribution shift. arXiv preprint arXiv:2006.14422 (2020)

  5. Guo, D., Dong, J., Wang, K.: Graph structure and statistical properties of ethereum transaction relationships. Inf. Sci., 492, 58–71 (2019). https://doi.org/10.1016/j.ins.2019.04.013, http://www.sciencedirect.com/science/article/pii/S0020025519303159

  6. Jablonski, J.A., Bihl, T.J., Bauer, K.W.: Principal component reconstruction error for hyperspectral anomaly detection. IEEE Geosci. Remote Sens. Lett. 12(8), 1725–1729 (2015)

    Article  Google Scholar 

  7. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  8. Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)

  9. Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. arXiv preprint arXiv:1801.07606 (2018)

  10. Lin, D., Wu, J., Yuan, Q., Zheng, Z.: Modeling and understanding ethereum transaction records via a complex network approach. Express Briefs, IEEE Trans. Circuits Syst. II (2020)

    Google Scholar 

  11. Lorenz, J., Silva, M.I., Aparício, D., Ascensão, J.T., Bizarro, P.: Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity (2020)

    Google Scholar 

  12. Pauwels, E.J., Ambekar, O.: One class classification for anomaly detection: support vector data description revisited. In: Perner, P. (ed.) ICDM 2011. LNCS (LNAI), vol. 6870, pp. 25–39. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23184-1_3

    Chapter  Google Scholar 

  13. Pham, T., Lee, S.: Anomaly detection in bitcoin network using unsupervised learning methods (2016)

    Google Scholar 

  14. Pham, T., Lee, S.: Anomaly detection in the bitcoin system – a network perspective. arXiv abs/1611.03942 (2016)

    Google Scholar 

  15. Praitheeshan, P., Pan, L., Yu, J., Liu, J., Doss, R.: Security analysis methods on ethereum smart contract vulnerabilities: a survey. arXiv preprint arXiv:1908.08605 (2019)

  16. Praitheeshan, P., Xin, Y.W., Pan, L., Doss, R.: Attainable hacks on keystore files in ethereum wallets-a systematic analysis. In: Proceedings of the International Conference on Future Network Systems and Security (FNSS’2019), pp. 99–117. Springer (2019). https://doi.org/10.1007/978-3-030-34353-8_7

  17. Puzyrev, V.: Deep convolutional autoencoder for cryptocurrency market analysis. arXiv preprint arXiv:1910.12281 (2019)

  18. Rajasegarar, S., Leckie, C., Bezdek, J.C., Palaniswami, M.: Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks. IEEE Trans. Inf. Forensics Secur. 5(3), 518–533 (2010)

    Article  Google Scholar 

  19. Ruff, L., et al.: Deep one-class classification. In: Proceedings of the International Conference on Machine Learning, pp. 4393–4402 (2018)

    Google Scholar 

  20. Sayadi, S., Rejeb, S.B., Choukair, Z.: Anomaly detection model over blockchain electronic transactions. In: Proceedings of the 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC’ 2019), pp. 895–900. IEEE (2019)

    Google Scholar 

  21. Spagnuolo, M., Maggi, F., Zanero, S.: BitIodine: extracting intelligence from the bitcoin network. In: Christin, N., Safavi-Naini, R. (eds.) FC 2014. LNCS, vol. 8437, pp. 457–468. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45472-5_29

    Chapter  Google Scholar 

  22. Tax, D.M., Duin, R.P.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)

    Article  Google Scholar 

  23. Verma, V., Qu, M., Lamb, A., Bengio, Y., Kannala, J., Tang, J.: Graphmix: Regularized training of graph neural networks for semi-supervised learning (2019)

    Google Scholar 

  24. Wang, X., Du, Y., Cui, P., Yang, Y.: OCGNN: One-class classification with graph neural networks. arXiv preprint arXiv:2002.09594 (2020)

  25. Zarpelão, B.B., Miani, R.S., Rajarajan, M.: Detection of bitcoin-based botnets using a one-class classifier. In: Blazy, O., Yeun, C.Y. (eds.) WISTP 2018. LNCS, vol. 11469, pp. 174–189. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20074-9_13

    Chapter  Google Scholar 

  26. Zheng, P., Zheng, Z., ning Dai, H.: Xblock-eth: Extracting and exploring blockchain data from ethereum (2019)

    Google Scholar 

  27. Zhou, Z., Zhang, S., Huang, Z.: Dynamic self-training framework for graph convolutional networks. arXiv preprint arXiv:1910.02684 (2019)

  28. Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: Proceedings of the International Conference on Learning Representations (2018)

    Google Scholar 

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Acknowledgement

We would like to thank Mr. Xuhong Wang, one of the authors of [24], for sharing the source code and answering our queries promptly. We are also very grateful for the valuable comments provided by anonymous reviewers.

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Authors and Affiliations

  1. School of IT, Deakin University, Geelong, VIC, 3220, Australia

    Vatsal Patel, Lei Pan & Sutharshan Rajasegarar

Authors
  1. Vatsal Patel
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  2. Lei Pan
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  3. Sutharshan Rajasegarar
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Corresponding author

Correspondence to Lei Pan .

Editor information

Editors and Affiliations

  1. Wrocław University of Technology, Wroclaw, Poland

    Mirosław Kutyłowski

  2. Swinburne University of Technology, Hawthorn, VIC, Australia

    Jun Zhang

  3. James Cook University, Douglas, QLD, Australia

    Chao Chen

Appendices

A OCGNN Model Training Algorithm

figure a

B Loss Curves and AUROC Curves for Different Training Sample Sizes

Fig. 3.
figure 3

Loss Curves and AUROC Curves for different training sample sizes

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Patel, V., Pan, L., Rajasegarar, S. (2020). Graph Deep Learning Based Anomaly Detection in Ethereum Blockchain Network. In: Kutyłowski, M., Zhang, J., Chen, C. (eds) Network and System Security. NSS 2020. Lecture Notes in Computer Science(), vol 12570. Springer, Cham. https://doi.org/10.1007/978-3-030-65745-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-65745-1_8

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