Abstract
The anonymity of blockchain has accelerated the growth of illegal activities and criminal behaviors on cryptocurrency platforms. Although decentralization is one of the typical characteristics of blockchain, we urgently call for effective regulation to detect these illegal behaviors to ensure the safety and stability of user transactions. Identity inference, which aims to make a preliminary inference about account identity, plays a significant role in blockchain security. As a common tool, graph mining technique can effectively represent the interactive information between accounts and be used for identity inference. However, existing methods cannot balance scalability and end-to-end architecture, resulting high computational consumption and weak feature representation. In this paper, we present a novel approach to analyze user’s behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern and effectively avoids computation in large-scale graph. Furthermore, we propose a generic end-to-end graph neural network model, named \(\text {I}^2 \text {BGNN}\), which can accept subgraph as input and learn a function mapping the transaction subgraph pattern to account identity, achieving de-anonymization. Extensive experiments on EOSG and ETHG datasets demonstrate that the proposed method achieve the state-of-the-art performance in identity inference.
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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
Wu, J., Lin, D., Zheng, Z., Yuan, Q.: T-edge: temporal weighted multidigraph embedding for ethereum transaction network analysis. arXiv preprint arXiv:1905.08038 (2019)
Chen, W., Zheng, Z., Cui, J., Ngai, E., Zheng, P., Zhou, Y.: Detecting ponzi schemes on ethereum: towards healthier blockchain technology. In: Proceedings of the 2018 World Wide Web Conference, pp. 1409–1418 (2018)
Bryans, D.: Bitcoin and money laundering: mining for an effective solution. Ind. LJ 89, 441 (2014)
Fanusie, Y., Robinson, T.: Bitcoin laundering: an analysis of illicit flows into digital currency services. Center on Sanctions and Illicit Finance memorandum, January 2018
Huang, Y., et al.: Understanding (mis) behavior on the eosio blockchain. Proc. ACM Meas. Anal. Comput. Syst. 4(2), 1–28 (2020)
Di Francesco, D., Maesa, A.M., Ricci, L.: Detecting artificial behaviours in the bitcoin users graph. Online Soc. Networks Media 3, 63–74 (2017)
Kondor, D., Pósfai, M., Csabai, I., Vattay, G.: Do the rich get richer? an empirical analysis of the bitcoin transaction network. PloS One 9(2), e86197 (2014)
Alqassem, I., Rahwan, I., Svetinovic, D.: The anti-social system properties: bitcoin network data analysis. IEEE Trans. Syst. Man Cybern. Syst. 50(1), 21–31 (2018)
Tasca, P., Hayes, A., Liu, S.: The evolution of the bitcoin economy: extracting and analyzing the network of payment relationships. J. Risk Financ. (2018)
Bai, Q., Zhang, C., Xu, Y., Chen, X., Wang, X.: Evolution of ethereum: a temporal graph perspective. arXiv preprint arXiv:2001.05251 (2020)
Ferretti, S., D’Angelo, G.: On the ethereum blockchain structure: a complex networks theory perspective. Concurrency Comput. Practice Exp. 32(12), e5493 (2020)
Huang, B., Liu, Z., Chen, J., Liu, A., Liu, Q., He, Q.: Behavior pattern clustering in blockchain networks. Multimed. Tools Appl. 76(19), 20099–20110 (2017). https://doi.org/10.1007/s11042-017-4396-4
Ranshous, S., et al.: Exchange pattern mining in the bitcoin transaction directed hypergraph. In: Brenner, M. (ed.) FC 2017. LNCS, vol. 10323, pp. 248–263. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70278-0_16
Yuan, Z., Yuan, Q., Wu, J.: Phishing detection on ethereum via learning representation of transaction subgraphs. In: Zheng, Z., Dai, H.-N., Fu, X., Chen, B. (eds.) BlockSys 2020. CCIS, vol. 1267, pp. 178–191. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-9213-3_14
Yuan, Q., Huang, B., Zhang, J., Wu, J., Zhang, H., Zhang, X.: Detecting phishing scams on ethereum based on transaction records. In: 2020 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5. IEEE (2020)
Li, Y., Cai, Y., Tian, H., Xue, G., Zheng, Z.: Identifying illicit addresses in bitcoin network. In: Zheng, Z., Dai, H.-N., Fu, X., Chen, B. (eds.) BlockSys 2020. CCIS, vol. 1267, pp. 99–111. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-9213-3_8
Pham, T., Lee, S.: Anomaly detection in the bitcoin system-a network perspective. arXiv preprint arXiv:1611.03942 (2016)
Wu, J., et al.: Who are the phishers? phishing scam detection on ethereum via network embedding. IEEE Trans. Syst. Man Cybern. Syst. (2020)
Wu, J., Liu, J., Chen, W., Huang, H., Zheng, Z., Zhang, Y.: Detecting mixing services via mining bitcoin transaction network with hybrid motifs. IEEE Trans. Syst. Man Cybern. Syst. (2021)
Tam, D.S.H., Lau, W.C., Hu, B., Ying, Q.F., Chiu, D.M., Liu, H.: Identifying illicit accounts in large scale e-payment networks-a graph representation learning approach. arXiv preprint arXiv:1906.05546 (2019)
Weber, M., et al.: Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591 (2019)
Lin, Y.-J., Wu, P.-W., Hsu, C.-H., Tu, I.-P., Liao, S.: An evaluation of bitcoin address classification based on transaction history summarization. In: 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pp. 302–310. IEEE (2019)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Narayanan, A., Chandramohan, M., Venkatesan, R., Chen, L., Liu, Y., Jaiswal, S.: graph2vec: learning distributed representations of graphs. arXiv preprint arXiv:1707.05005 (2017)
Xuan, Q., et al.: Subgraph networks with application to structural feature space expansion. IEEE Trans. Knowl. Data Eng. (2019)
Chen, L., Peng, J., Liu, Y., Li, J., Xie, F., Zheng, Z.: Phishing scams detection in ethereum transaction network. ACM Trans. Internet Technol. (TOIT) 21(1), 1–16 (2020)
Ke, G., et al.: Lightgbm: a highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30, 3146–3154 (2017)
Phetsouvanh, S., Oggier, F., Datta, A.: Egret: extortion graph exploration techniques in the bitcoin network. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 244–251. IEEE (2018)
Zhang, R., Zhang, G., Liu, L., Wang, C., Wan, S.: Anomaly detection in bitcoin information networks with multi-constrained meta path. J. Syst. Archit. 110, 101829 (2020)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018). Accepted as poster
Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263–1272. PMLR (2017)
de Lara, N., Pineau, E.: A simple baseline algorithm for graph classification. arXiv preprint arXiv:1810.09155 (2018)
Tsitsulin, A., Mottin, D., Karras, P., Bronstein, A., Müller, E.: Netlsd: hearing the shape of a graph. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2347–2356 (2018)
Verma, S., Zhang, Z.-L.: Hunt for the unique, stable, sparse and fast feature learning on graphs. In: NIPS, pp. 88–98 (2017)
Acknowledgements
The authors would like to thank all the members in the IVSN Research Group, Zhejiang University of Technology for the valuable discussions about the ideas and technical details presented in this paper. This work was partially supported by the National Key R&D Program of China under Grant No. 2020YFB1006104, by the National Natural Science Foundation of China under Grant No. 61973273, by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LR19F030001, by the Ministry of Public Security’s Research Project “Research and Demonstration Application of Key Technologies of Criminal Social Network Model”, and by the Special Scientific Research Fund of Basic Public Welfare Profession of Zhejiang Province under Grant LGF20F020016.
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Shen, J., Zhou, J., Xie, Y., Yu, S., Xuan, Q. (2021). Identity Inference on Blockchain Using Graph Neural Network. In: Dai, HN., Liu, X., Luo, D.X., Xiao, J., Chen, X. (eds) Blockchain and Trustworthy Systems. BlockSys 2021. Communications in Computer and Information Science, vol 1490. Springer, Singapore. https://doi.org/10.1007/978-981-16-7993-3_1
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