Abstract
The emergence of blockchain-based anonymous and encrypted digital currencies has brought with it a rapid increase in financial crimes. However, the regulation and detection of financial crimes requires the detection of abnormal transactions in the scenario of blockcain-based anonymous and encrypted digital currencies, where traditional methods are not applicable. In this paper, we propose an abnormal transaction node detection method on bitcoin based on outlier ranking of transaction communities. The public key addresses in bitcoin transactions are merged according to whether they belong to the same user in order to form a user transaction graph, which is used as the input of our method. This graph is then divided into smaller communities. The abnormal transaction nodes are detected by ranking each node with its inter/intra-community link outlier value. By conducting experiments on a subset of bitcoin transactions, it shows that the proposed method is able to effectively detect known abnormal nodes involved in financial crimes.
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Zhang, Y., Lu, Y., Li, M. (2024). Abnormal Transaction Node Detection on Bitcoin. In: Zhang, Y., Qi, L., Liu, Q., Yin, G., Liu, X. (eds) Proceedings of the 13th International Conference on Computer Engineering and Networks. CENet 2023. Lecture Notes in Electrical Engineering, vol 1127. Springer, Singapore. https://doi.org/10.1007/978-981-99-9247-8_6
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DOI: https://doi.org/10.1007/978-981-99-9247-8_6
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