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Shoot Before You Escape: Dynamic Behavior Monitor of Bitcoin Users via Bi-Temporal Network Analytics

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Information Security and Privacy (ACISP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13494))

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Abstract

As the first successful decentralized cryptocurrency system, Bitcoin has gradually become a breeding ground for hiding illegal or malicious activities without a central governing authority, in recent years. It remains a challenging task to mine Bitcoin blockchain for better financial forensics and security regulation, due to the hugeness and dynamism of transaction data. In this paper, we propose BitMonitor, which enables dynamic classification of Bitcoin users in real-time. The key module of BitMonitor is the construction of Bi-Temporal transaction network. Through it, on the one hand, we can perform temporal slicing of transactions associated with wallet nodes and sequentially tag the intent of user activity at different stages. On the other hand, it enables deep backtracking and tracing of the relevant financial flows dynamically over time. We demonstrate the effectiveness of the resulting multi-order neighborhood information in a static experimental scenario. Besides, the specificity of the Bi-Temporal transaction network also allows for incremental updating of neighborhood characteristics. Finally, through a weighted voting mechanism by incorporating tags on historical slices, we evaluate the dynamic classification performance from two different aspects. Unlike the static post-mortem analysis among existing work, we are the first to conduct a dynamic behavior monitor for the purpose of identifying Bitcoin accounts as soon as possible.

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Acknowledgement

This work is supported by The National Key Research and Development Program of China No. 2021YFB3101400 and the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDC02040400.

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Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Chen Zhao, Jianing Ding, Zhenzhen Li, Zhen Li, Gang Xiong & Gaopeng Gou

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Chen Zhao, Jianing Ding, Zhenzhen Li, Zhen Li, Gang Xiong & Gaopeng Gou

Authors
  1. Chen Zhao
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  2. Jianing Ding
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  3. Zhenzhen Li
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  4. Zhen Li
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  5. Gang Xiong
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Corresponding author

Correspondence to Gaopeng Gou .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Khoa Nguyen

  2. University of Wollongong, Wollongong, NSW, Australia

    Guomin Yang

  3. University of Wollongong, Wollongong, NSW, Australia

    Fuchun Guo

  4. University of Wollongong, Wollongong, NSW, Australia

    Willy Susilo

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Zhao, C., Ding, J., Li, Z., Li, Z., Xiong, G., Gou, G. (2022). Shoot Before You Escape: Dynamic Behavior Monitor of Bitcoin Users via Bi-Temporal Network Analytics. In: Nguyen, K., Yang, G., Guo, F., Susilo, W. (eds) Information Security and Privacy. ACISP 2022. Lecture Notes in Computer Science, vol 13494. Springer, Cham. https://doi.org/10.1007/978-3-031-22301-3_25

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  • DOI: https://doi.org/10.1007/978-3-031-22301-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-22300-6

  • Online ISBN: 978-3-031-22301-3

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