Abstract
We analyze the magnitude of illicit activities in the Ethereum ecosystem. Using proprietary labeling data from the Blockchain Intelligence Group (BIG), we investigate the characteristics of a number of “malicious” Ethereum addresses. We first calculate the total number of transactions involving these addresses and the total amount of funds transferred through them, and then characterize smart contract addresses for ERC-20 tokens or DeFi applications, that the malicious addresses interact with. Finally, we apply machine learning techniques to identify additional “malicious” addresses by conducting a network clustering analysis within all Ethereum addresses from transactional relationships with the initial set of malicious addresses.
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Acknowledgements
The GMU authors of this paper were supported by a US Department of Homeland Security award #205187 through the Criminal Investigations and Network Analysis Center (CINA).
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© 2021 International Financial Cryptography Association
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Li, J. et al. (2021). Measuring Illicit Activity in DeFi: The Case of Ethereum. In: Bernhard, M., et al. Financial Cryptography and Data Security. FC 2021 International Workshops. FC 2021. Lecture Notes in Computer Science(), vol 12676. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-63958-0_18
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DOI: https://doi.org/10.1007/978-3-662-63958-0_18
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