Abstract
We conducted a longitudinal study to analyze the misuse of Bitcoin. We first investigated usage characteristics of Bitcoin by analyzing how many addresses each address transacts with (from January 2009 to May 2018). To obtain a quantitative estimate of the malicious activity that Bitcoin is associated with, we collected over 2.3 million candidate Bitcoin addresses, harvested from the dark web between June 2016 and December 2017. The Bitcoin addresses found on the dark web were labeled with tags that classified the activities associated with the onions that these addresses were collected from. The tags covered a wide range of activities, from suspicious to outright malicious or illegal. Of these addresses, only 47,697 have tags we consider indicative of suspicious or malicious activities.
We saw a clear decline in the monthly number of Bitcoin addresses seen on the dark web in the periods coinciding with takedowns of known dark web markets. We also found interesting behavior that distinguishes the Bitcoin addresses collected from the dark web when compared to activity of a random address on the Bitcoin blockchain. For example, we found that Bitcoin addresses used on the dark web are more likely to be involved in mixing transactions. To identify mixing transactions, we developed a new heuristic that extends previously known ones. We found that Bitcoin addresses found on the dark web are significantly more active, they engage in transactions with 20 times the neighbors and 4 times the Bitcoin amounts when compared to random addresses. We also found that just 2,828 Bitcoin addresses are responsible for 99% of the Bitcoin value used on the dark web.
This material is based upon work supported by the National Science Foundation (NSF) under Grant ACI-1547467. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.
A. Matton—Research performed while visiting SRI.
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Eldefrawy, K., Gehani, A., Matton, A. (2019). Longitudinal Analysis of Misuse of Bitcoin. In: Deng, R., Gauthier-Umaña, V., Ochoa, M., Yung, M. (eds) Applied Cryptography and Network Security. ACNS 2019. Lecture Notes in Computer Science(), vol 11464. Springer, Cham. https://doi.org/10.1007/978-3-030-21568-2_13
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