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CTDM: cryptocurrency abnormal transaction detection method with spatio-temporal and global representation

  • Soft computing in decision making and in modeling in economics
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Abstract

With the rapid advances in computing and networking technologies, there have led to the creation of a novel and booming set of payment services, known as cryptocurrencies or digital tokens. Many are available for exchanges worldwide, inviting investors to trade with costs, quality, and safety that vary widely. Nevertheless, Blockchain transaction data have complex time and space dependencies, and historical transaction data reflect the transaction trends of cryptocurrencies to a certain extent, thus identifying the illegal behaviors of transactions such as money laundering more at the earliest. In this article, we propose a novel cryptocurrency abnormal transaction detection method with spatio-temporal and global representation, namely CTDM. CTDM combines EvolveGCN with MGU and global representations to achieve better performance. In addition, CTDM needs fewer learning parameters through MGU, which leads to less training time. Experimental results show that the proposed CTDM method outperforms SOTA Blockchain abnormal transaction detection methods.

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Data availability

All experiments in this article are based on publicly available datasets: The Elliptic dataset (Weber et al. 2019) and the Bitcoin OTC dataset.

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Funding

This work is supported in part by the National Natural Science Foundation of China under grants 61672338 and 61873160.

Author information

Authors and Affiliations

  1. College of Information Engineering at Shanghai Maritime University, Shanghai, China

    Lijun Xiao, Dezhi Han & Dun Li

  2. School of Computer Science and Engineering, Hunan University of Science and Technology, and the Hunan Key Laboratory for Service computing and Novel Software Technology, Xiangtan, China

    Wei Liang & Ce Yang

  3. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

  4. Department of Computer Science, University of Salerno, 84084, Fisciano, Italy

    Arcangelo Castiglione

Authors
  1. Lijun Xiao
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  2. Dezhi Han
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  6. Kuan-Ching Li
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Correspondence to Kuan-Ching Li.

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Xiao, L., Han, D., Li, D. et al. CTDM: cryptocurrency abnormal transaction detection method with spatio-temporal and global representation. Soft Comput 27, 11647–11660 (2023). https://doi.org/10.1007/s00500-023-08220-x

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