Abstract
Bitcoin has attracted a lot of attentions from both researchers and investors since it was first proposed in 2008. One of the key characteristics of Bitcoin is anonymity, which makes the Bitcoin market unregulated and a large number of criminal and illicit activities are associated with bitcoin transactions. Therefore, it’s necessary to identify the illicit addresses in the Bitcoin network for safeguarding financial systems and protecting user’s assets. To identify the illicit addresses in the Bitcoin network, first, we collect a large dataset of illicit addresses. The illicit addresses come mainly from some specific websites, public forums, and research papers. Second, we make a careful design of the features of illicit addresses. The features include basic features that refer to the related papers and the novel proposed features (topological features and temporal features). Third, we apply various machine learning algorithms (RF, SVM, XGB, ANN) to evaluate our features, which indicates that the proposed features are discriminating and robust. Besides, the paper discusses the class imbalance problem and achieves a better enhancement when using the cost-sensitive approach. Moreover, the paper proposes a model that incorporates LSTM into auto-encoder to generate temporal features. Results show that the generated features are helpful for the illicit addresses identification. Finally, the dataset and code are released in Github.
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References
Nakamoto, S., et al.: Bitcoin: A peer-to-peer electronic cash system (2008)
Zheng, Z., Xie, S., Dai, H., Chen, X., Wang, H.: An overview of blockchain technology: Architecture, consensus, and future trends. In: 2017 IEEE International Congress on Big Data (BigData Congress), pp. 557–564. IEEE (2017)
Zheng, Z., Xie, S., Dai, H.-N., Chen, X., Wang, H.: Blockchain challenges and opportunities: a survey. Int. J. Web Grid Serv. 14(4), 352–375 (2018)
Chuen, D.L.K.: Handbook of Digital Currency: Bitcoin, Innovation, Financial Instruments, and Big Data. Academic Press, Cambridge (2015)
Eyal, I., Sirer, E.G.: Majority is not enough: bitcoin mining is vulnerable. Commun. ACM 61(7), 95–102 (2018)
Reid, F., Harrigan, M.: An analysis of anonymity in the bitcoin system. In: Altshuler, Y., Elovici, Y., Cremers, A., Aharony, N., Pentland, A. (eds.) Security and Privacy in Social Networks, pp. 197–223. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-4139-7_10
Hurlburt, G.F., Bojanova, I.: Bitcoin: Benefit or curse? It Professional, 16(3), 10–15 (2014)
Foley, S., Karlsen, J.R., Putninš, T.J.: Sex, drugs, and bitcoin: how much illegal activity is financed through cryptocurrencies? Rev. Financ. Stud. 32(5), 1798–1853 (2019)
Janze, C.: Are cryptocurrencies criminals best friends? Examining the co-evolution of bitcoin and darknet markets (2017)
Pham, T., Lee, S.: Anomaly detection in bitcoin network using unsupervised learning methods. arXiv preprint arXiv:1611.03941 (2016)
Monamo, P., Marivate, V., Twala, B.: Unsupervised learning for robust bitcoin fraud detection. In: 2016 Information Security for South Africa (ISSA), pp. 129–134. IEEE (2016)
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)
Weber, M., et al.: Anti-money laundering in bitcoin: experimenting with graph convolutional networks for financial forensics. arXiv preprint arXiv:1908.02591 (2019)
Bartoletti, M., Pes, B., Serusi, S.: Data mining for detecting bitcoin Ponzi schemes. In: 2018 Crypto Valley Conference on Blockchain Technology (CVCBT), pp. 75–84. IEEE (2018)
Toyoda, K., Takis Mathiopoulos, P., Ohtsuki, T.: A novel methodology for hyip operators’ bitcoin addresses identification. IEEE Access 7, 74835–74848 (2019)
Liao, K., Zhao, Z., Doupé, A., Ahn, G.-J.: Behind closed doors: measurement and analysis of cryptolocker ransoms in bitcoin. In: 2016 APWG Symposium on Electronic Crime Research (eCrime), pp. 1–13. IEEE (2016)
Paquet-Clouston, M., Haslhofer, B., Dupont, B.: Ransomware payments in the bitcoin ecosystem. J. Cybersecur. 5(1), tyz003 (2019)
Androulaki, E., Karame, G.O., Roeschlin, M., Scherer, T., Capkun, S.: Evaluating user privacy in bitcoin. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 34–51. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39884-1_4
Toyoda, K., Ohtsuki, T., Takis Mathiopoulos, P.: Multi-class bitcoin-enabled service identification based on transaction history summarization. In: 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp. 1153–1160. IEEE (2018)
Jourdan, M., Blandin, S., Wynter, L., Deshpande, P.: Characterizing entities in the bitcoin blockchain. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 55–62. IEEE (2018)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Hearst, M.A., Dumais, S.T., Osuna, E., Platt, J., Scholkopf, B.: Support vector machines. IEEE Intell. Syst. Appl. 13(4), 18–28 (1998)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794. ACM (2016)
Jain, A.K., Mao, J., Moidin Mohiuddin, K.: Artificial neural networks: a tutorial. Computer 29(3), 31–44 (1996)
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Gulli, A., Pal, S.: Deep Learning with Keras. Packt Publishing Ltd. (2017)
Longadge, R., Dongre, S.: Class imbalance problem in data mining review. arXiv preprint arXiv:1305.1707 (2013)
Nguyen, G.H., Bouzerdoum, A., Phung, S.L.: Learning pattern classification tasks with imbalanced data sets. In Pattern recognition, IntechOpen (2009)
Sun, Y., et al.: Cost-sensitive boosting for classification of imbalanced data. Pattern Recogn. 40(12), 3358–3378 (2007)
Bahnsen, A.C.: Ensembles of example-dependent cost-sensitive decision trees (2015)
Kramer, M.A.: Nonlinear principal component analysis using auto associative neural networks. AIChE J. 37(2), 233–243 (1991)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Acknowledgments
The work described in this paper was supported by the National Key Research and Development Program (2016YFB1000101), the National Natural Science Foundation of China (U1811462, 61722214) and the Key-Area Research and Development Program of Guangdong Province (2018B010109001).
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Li, Y., Cai, Y., Tian, H., Xue, G., Zheng, Z. (2020). Identifying Illicit Addresses in Bitcoin Network. In: Zheng, Z., Dai, HN., Fu, X., Chen, B. (eds) Blockchain and Trustworthy Systems. BlockSys 2020. Communications in Computer and Information Science, vol 1267. Springer, Singapore. https://doi.org/10.1007/978-981-15-9213-3_8
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DOI: https://doi.org/10.1007/978-981-15-9213-3_8
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