Time series analysis for bitcoin transactions: The case of Pirate@40's HYIP scheme

Kentaro Toyoda, Tomoaki Ohtsuki, P. Takis Mathiopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Due to the increased popularity of Bitcoin, many researchers have analyzed how Bitcoin is being used based on the transaction history. However, the existing works analyze the transaction history in a 'static' manner and none of them analyzes transaction history 'dynamically', i.e. without taking into account the 'time variation of how Bitcoin is transferred'. The time analysis is in great demand for many practical cases, such as digital forensics tool that infers what was going on behind the scene of a fraudulent scam, and real-time inference of marketplace sales. In this paper, we propose a novel time series analysis for analyzing the history of Bitcoin transactions. In fact the main goal of our research is to detect changing points, namely anomaly detection, against a given (Bitcoin) address's transaction history. To show the effectiveness of the proposed approach, it is tested against the transaction history of Pirate@40's HYIP (High Yielding Investment Program) scheme, which raised 700,000 BTC from his investors and was charged by the Security and Exchange Commission (SEC) in 2013. It is shown that the proposed approach can successfully detect several remarkable points of Pirate@40's HYIP scheme, such as when its program's name was changed to Bitcoin Saving & Trust and when its investment rule was changed.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
EditorsJeffrey Yu, Zhenhui Li, Hanghang Tong, Feida Zhu
PublisherIEEE Computer Society
Pages151-155
Number of pages5
ISBN (Electronic)9781538692882
DOIs
Publication statusPublished - 2019 Feb 7
Event18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore
Duration: 2018 Nov 172018 Nov 20

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2018-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
CountrySingapore
CitySingapore
Period18/11/1718/11/20

Keywords

  • Bitcoin
  • Data Mining
  • Fraud Detection
  • Transaction Analysis

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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  • Cite this

    Toyoda, K., Ohtsuki, T., & Mathiopoulos, P. T. (2019). Time series analysis for bitcoin transactions: The case of Pirate@40's HYIP scheme. In J. Yu, Z. Li, H. Tong, & F. Zhu (Eds.), Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 (pp. 151-155). [8637383] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2018-November). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2018.00028