IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v16y2023i9p408-d1239096.html
   My bibliography  Save this article

Change Point Analysis of Time Series Related to Bitcoin Transactions: Towards the Detection of Illegal Activities

Author

Listed:
  • Ourania Theodosiadou

    (Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece)

  • Alexandros-Michail Koufakis

    (Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece)

  • Theodora Tsikrika

    (Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece)

  • Stefanos Vrochidis

    (Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece)

  • Ioannis Kompatsiaris

    (Information Technologies Institute, Centre for Research and Technology Hellas, 57001 Thessaloniki, Greece)

Abstract

This paper proposes a unified framework for the detection of statistically significant changes in time series related to Bitcoin transactions. The time locations of these changes are linked to the occurrences of events which could be further investigated aiming to reveal potential illicit activity. The proposed framework includes: (a) the extraction of 28 features of interest in the form of time series from the Bitcoin transaction history; (b) the selection of features among the extracted ones based on the Partition Around Medoids clustering approach; and (c) the change point analysis of the multivariate time series which is formulated by the medoid time series of each cluster. This analysis enables the identification of structural breaks in the underlying behavior of the time series of interest at certain time points. The proposed framework is applied on the Bitcoin transactions of two entities that have been involved in illicit activities, namely Pirate@40 , who orchestrated a high-yield investment programme, and the MintPal Bitcoin exchange platform that was hacked. The analysis results indicate that the estimated change points can be linked to certain event occurrences which may affect the transaction activity and could be further investigated for potential links to illicit actions.

Suggested Citation

  • Ourania Theodosiadou & Alexandros-Michail Koufakis & Theodora Tsikrika & Stefanos Vrochidis & Ioannis Kompatsiaris, 2023. "Change Point Analysis of Time Series Related to Bitcoin Transactions: Towards the Detection of Illegal Activities," JRFM, MDPI, vol. 16(9), pages 1-20, September.
  • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:9:p:408-:d:1239096
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/16/9/408/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/16/9/408/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mark Weber & Giacomo Domeniconi & Jie Chen & Daniel Karl I. Weidele & Claudio Bellei & Tom Robinson & Charles E. Leiserson, 2019. "Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics," Papers 1908.02591, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    2. Zeinab Rouhollahi, 2021. "Towards Artificial Intelligence Enabled Financial Crime Detection," Papers 2105.10866, arXiv.org.
    3. Nasir Sultan & Norazida Mohamed & Mervyn Martin & Hafizah Mohd Latif, 2023. "Virtual currencies and money laundering: existing and prospects for jurisdictions that comprehensively prohibited virtual currencies like Pakistan," Journal of Money Laundering Control, Emerald Group Publishing Limited, vol. 27(2), pages 395-412, May.
    4. Yang, Guo-Hui & Zhong, Guang-Yan & Wang, Li-Ya & Xie, Zu-Guang & Li, Jiang-Cheng, 2024. "A hybrid forecasting framework based on MCS and machine learning for higher dimensional and unbalanced systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    5. Alexander Wong & Andrew Hryniowski & Xiao Yu Wang, 2020. "Insights into Fairness through Trust: Multi-scale Trust Quantification for Financial Deep Learning," Papers 2011.01961, arXiv.org.
    6. Wai Weng Lo & Gayan K. Kulatilleke & Mohanad Sarhan & Siamak Layeghy & Marius Portmann, 2022. "Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin," Papers 2203.10465, arXiv.org, revised Oct 2022.
    7. Claudio Bellei & Muhua Xu & Ross Phillips & Tom Robinson & Mark Weber & Tim Kaler & Charles E. Leiserson & Arvind & Jie Chen, 2024. "The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset," Papers 2404.19109, arXiv.org, revised Jul 2024.
    8. Jianian Wang & Sheng Zhang & Yanghua Xiao & Rui Song, 2021. "A Review on Graph Neural Network Methods in Financial Applications," Papers 2111.15367, arXiv.org, revised Apr 2022.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jjrfmx:v:16:y:2023:i:9:p:408-:d:1239096. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.