Abstract
Blockchain technology, with its decentralised peer-to-peer network and cryptographic protocols, has led to a proliferation of cryptocurrencies, with Bitcoin at the forefront. The blockchain publicly records all Bitcoin transactions which can be used to build a dynamic and complex network to give a representation of the transactions in the underlying monetary system. Despite the cryptographic guarantees there exist inconsistencies and suspicious behavior in the chain. We reported on two such anomalies related to block mining in previous work. In this paper, we build a network using bitcoin transactions and apply techniques from network science to analyse its complex structure. We focus our analysis on sub-networks induced by the two sets of anomalies, and investigate how inequality in terms of wealth and anomaly fraction evolves from the blockchain’s origin. Thereby we present a novel way of using network science to detect and investigate cryptographic anomalies.
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Notes
- 1.
Elliptic is a cryptocurrency intelligence company focused on safeguarding cryptocurrency ecosystems from criminal activity.
- 2.
The paper is currently under review, but will be shared upon request.
References
Alarab, I., Prakoonwit, S., Nacer, M.I.: Comparative analysis using supervised learning methods for anti-money laundering in bitcoin. In: Proceedings of the 2020 5th International Conference on Machine Learning Technologies, pp. 11–17 (2020)
Barabási, A., et al.: Network Science. Cambridge University Press, Cambridge (2016)
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)
Bovet, A., Campajola, C., Mottes, F., Restocchi, V., Vallarano, N., Squartini, T., Tessone, C.J.: The evolving liaisons between the transaction networks of bitcoin and its price dynamics (2019). arXiv preprint: arXiv:1907.03577
Courtois, N.T., Grajek, M., Naik, R.: The unreasonable fundamental incertitudes behind bitcoin mining (2013). arXiv preprint: arXiv:1310.7935
Dev, J.A.: Bitcoin mining acceleration and performance quantification. In: 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–6. IEEE (2014)
Eyal, I., Sirer, E.G.: Majority is not enough: bitcoin mining is vulnerable. In: Christin, N., Safavi-Naini, R. (eds.) Financial Cryptography and Data Security. FC 2014. Lecture Notes in Computer Science, vol. 8437, pp. 436–454. Springer, Berlin, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45472-5_28
Hu, Y., Seneviratne, S., Thilakarathna, K., Fukuda, K., Seneviratne, A.: Characterizing and detecting money laundering activities on the bitcoin network (2019). arXiv preprint: arXiv:1912.12060
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) (2014)
Li, S.-N., Yang, Z., Tessone, C.J.: Mining blocks in a row: a statistical study of fairness in bitcoin mining. In: 2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), pp. 1–4. IEEE (2020)
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). arXiv preprint: arXiv:2005.14635
Mallett, J.: A report on cryptographic anomalies in the bitcoin blockchain (2020)
McGinn, Dan, McIlwraith, Doug, Guo, Yike: Towards open data blockchain analytics: a bitcoin perspective. R. Soc. Open Sci. 5(8), 180298 (2018)
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)
Monamo, P.M., Marivate, V., Twala, B.: A multifaceted approach to bitcoin fraud detection: global and local outliers. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 188–194. IEEE (2016)
Nakamoto, S., Bitcoin, A.: A peer-to-peer electronic cash system. Bitcoin (2008). https://bitcoin.org/bitcoin.pdf
Pavithran, D., Al-Karaki, J.N., Thomas, R., Shibu, C., Gawanmeh, A.: Data-driven analysis of price change, user behavior and wealth accumulation in bitcoin transactions. In: 2019 Advances in Science and Engineering Technology International Conferences (ASET), pp. 1–6. IEEE (2019)
Pham, T., Lee, S.: Anomaly detection in the bitcoin system-a network perspective (2016). arXiv preprint: arXiv:1611.03942
Ron, D., Shamir, A.: Quantitative analysis of the full bitcoin transaction graph. In: Sadeghi, A.R. (ed.) Financial Cryptography and Data Security FC 2013. Lecture Notes in Computer Science, vol. 7859. Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39884-1_2
Turner, A.B., McCombie, S., Uhlmann, A.J.: Discerning payment patterns in bitcoin from ransomware attacks. Journal of Money Laundering Control (2020)
Vallarano, N., Tessone, C., Squartini, T.: Bitcoin transaction networks: an overview of recent results (2020). arXiv preprint: arXiv:2005.00114
Weber, M., Domeniconi, G., Chen, J., Weidele, D.K.I., Bellei, C., Robinson, T., Leiserson, C.E.: Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics (2019). arXiv preprint: arXiv:1908.02591
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Óskarsdóttir, M., Mallett, J., Arnarson, .L., Stefánsson, A.S. (2021). Analysis of Tainted Transactions in the Bitcoin Blockchain Transaction Network. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-65351-4_46
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