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A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning

Author

Listed:
  • Mohammad Javad Rajaei

    (Department of Electrical, Computer, and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada)

  • Qusay H. Mahmoud

    (Department of Electrical, Computer, and Software Engineering, Ontario Tech University, Oshawa, ON L1G 0C5, Canada)

Abstract

The popularity of cryptocurrencies has skyrocketed in recent years, with blockchain technologies enabling the development of new digital assets. However, along with their advantages, such as lower transaction costs, increased security, and transactional transparency, cryptocurrencies have also become susceptible to various forms of market manipulation. The pump and dump (P&D) scheme is of significant concern among these manipulation tactics. Despite the growing awareness of P&D activities in cryptocurrency markets, a comprehensive survey is needed to explore the detection methods. This paper aims to fill this gap by reviewing the literature on P&D detection in the cryptocurrency world. This survey provides valuable insights into detecting and classifying P&D schemes in the cryptocurrency market by analyzing the selected studies, including their definitions and the taxonomies of P&D schemes, the methodologies employed, their strengths and weaknesses, and the proposed solutions. Presented here are insights that can guide future research in this field and offer practical approaches to combating P&D manipulations in cryptocurrency trading.

Suggested Citation

  • Mohammad Javad Rajaei & Qusay H. Mahmoud, 2023. "A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning," Future Internet, MDPI, vol. 15(8), pages 1-17, August.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:8:p:267-:d:1214902
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    References listed on IDEAS

    as
    1. Sihao Hu & Zhen Zhang & Shengliang Lu & Bingsheng He & Zhao Li, 2022. "Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump," Papers 2204.12929, arXiv.org, revised Apr 2023.
    2. Nurullah Celal Uslu & Fuat Akal, 2022. "A Machine Learning Approach to Detection of Trade-Based Manipulations in Borsa Istanbul," Computational Economics, Springer;Society for Computational Economics, vol. 60(1), pages 25-45, June.
    3. Friedhelm Victor & Andrea Marie Weintraud, 2021. "Detecting and Quantifying Wash Trading on Decentralized Cryptocurrency Exchanges," Papers 2102.07001, arXiv.org.
    4. Massimo La Morgia & Alessandro Mei & Francesco Sassi & Julinda Stefa, 2020. "Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations," Papers 2005.06610, arXiv.org.
    5. Álvaro Cartea & Sebastian Jaimungal & Yixuan Wang, 2020. "Spoofing and Price Manipulation in Order-Driven Markets," Applied Mathematical Finance, Taylor & Francis Journals, vol. 27(1-2), pages 67-98, July.
    6. Marwa Alyami & Reem Alhotaylah & Sawsan Alshehri & Abdullah Alghamdi, 2023. "Phishing Attacks on Cryptocurrency Investors in the Arab States of the Gulf," JRFM, MDPI, vol. 16(5), pages 1-14, May.
    7. Jiahua Xu & Benjamin Livshits, 2018. "The Anatomy of a Cryptocurrency Pump-and-Dump Scheme," Papers 1811.10109, arXiv.org, revised Aug 2019.
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