Abstract
Bitcoin is expected to play a vital role in the web3 and is attracting attention. Still, it is notorious as a hotbed for various fraudulent transactions, which are on the rise. One of the characteristics of Bitcoin is its anonymity. Bitcoin allows tracing transactions, but the owner cannot be identified. In addition, bitcoin is considered more difficult to trace criminals than bank transactions because of the unlimited number of addresses that can be created. This study shows that it is possible to detect criminal transactions with known transaction patterns using supervised learning. Still, it is difficult to detect criminal transactions with unknown transaction patterns using supervised learning. We also show that even with anomaly detection methods, illicit transactions are difficult to detect. From the above, we believe that Bitcoin is vulnerable to illicit transactions.
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Acknowledgements
This research was supported by Telecommunications Advancement Foundation and JSPS KAKENHI Grant Number JP20K01751.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Ehara, T., Takahashi, H. (2023). Bitcoin Fraudulent Transaction Detection Vulnerability. In: Jezic, G., Chen-Burger, J., Kusek, M., Sperka, R., Howlett, R.J., Jain, L.C. (eds) Agents and Multi-agent Systems: Technologies and Applications 2023. KES-AMSTA 2023. Smart Innovation, Systems and Technologies, vol 354. Springer, Singapore. https://doi.org/10.1007/978-981-99-3068-5_17
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DOI: https://doi.org/10.1007/978-981-99-3068-5_17
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