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Machine Learning Based Bitcoin Address Classification

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Blockchain and Trustworthy Systems (BlockSys 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1267))

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Abstract

A bitcoin address is required for trading and maintaining pseudonymity for the owner. By exploiting this pseudonymity, various illegal activities are conducted around the world. To detect and deter illegal transactions, this paper proposes a method of identifying the characteristics of bitcoin addresses related to illegal transactions. We extracted 80 features from bitcoin transactions. Using machine-learning techniques, we successfully categorized addresses involved with illegal activities with a \(\sim \)84% accuracy. We also examined the address features most affecting classification performance and compared two machine-learning models. By applying the majority voting to the classification results of bitcoin addresses associated with a particular transaction, it will be possible to determine which category the transaction belongs to.

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Acknowledgments

This work was supported by the ICT R&D program of MSIT/IITP. [No.2018-0-00539, Development of Blockchain Transaction Monitoring and Analysis Technology].

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Correspondence to Chaehyeon Lee .

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Lee, C., Maharjan, S., Ko, K., Woo, J., Hong, J.WK. (2020). Machine Learning Based Bitcoin Address Classification. 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_40

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  • DOI: https://doi.org/10.1007/978-981-15-9213-3_40

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  • Print ISBN: 978-981-15-9212-6

  • Online ISBN: 978-981-15-9213-3

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