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Blockchain and crypto forensics: Investigating crypto frauds

Author

Listed:
  • Udit Agarwal
  • Vinay Rishiwal
  • Sudeep Tanwar
  • Mano Yadav

Abstract

In the past few years, cryptocurrency has gained widespread acceptance because of its decentralized nature, quick and secure transactions, and potential for investment and speculation. But the increased popularity has also led to increased cryptocurrency fraud, including scams, phishing attacks, Ponzi schemes, and other criminal activities. Although there is little documentation of cryptocurrency fraud, an in‐depth study is essential to recognize various scams in different cryptocurrencies. To fill this gap, a study investigated cryptocurrency‐related fraud in various cryptocurrencies and provided a taxonomy of crypto‐forensics and forensic blockchain. In addition, we have introduced an architecture that integrates artificial intelligence (AI) and blockchain technologies to investigate and protect against instances of cryptocurrency fraud. The suggested design's effectiveness was evaluated using several machine learning (ML) classification algorithms. The conclusion of the evaluation confirmed that the random forest (RF) classifier performed the best, delivering the highest level of accuracy, that is, 97.5%. Once the ML classifiers detect cryptocurrency fraud, the information is securely stored in the InterPlanetary File System (IPFS); the document's hash is also stored in the blockchain using smart contracts. Law enforcement can leverage blockchain technology to secure access to fraudulent cryptographic transactions. The proposed architecture was tested for bandwidth utilization. Despite the potential benefits of blockchain and crypto‐forensics, several issues and challenges remain, including privacy concerns, standardization, and difficulty identifying fraud between crypto‐currencies. Finally, the paper discusses various problems and challenges in blockchain and crypto forensics to investigate cryptocurrency fraud.

Suggested Citation

  • Udit Agarwal & Vinay Rishiwal & Sudeep Tanwar & Mano Yadav, 2024. "Blockchain and crypto forensics: Investigating crypto frauds," International Journal of Network Management, John Wiley & Sons, vol. 34(2), March.
    • Handle: RePEc:wly:intnem:v:34:y:2024:i:2:n:e2255
    DOI: 10.1002/nem.2255
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    References listed on IDEAS

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    1. Bruno Mazorra & Victor Adan & Vanesa Daza, 2022. "Do Not Rug on Me: Leveraging Machine Learning Techniques for Automated Scam Detection," Mathematics, MDPI, vol. 10(6), pages 1-24, March.
    2. Lamprini Zarpala & Fran Casino, 2021. "A blockchain-based forensic model for financial crime investigation: the embezzlement scenario," Digital Finance, Springer, vol. 3(3), pages 301-332, December.
    3. Sean Foley & Jonathan R Karlsen & Tālis J Putniņš, 2019. "Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1798-1853.
    4. Lars Hornuf & Theresa Kück & Armin Schwienbacher, 2022. "Initial coin offerings, information disclosure, and fraud," Small Business Economics, Springer, vol. 58(4), pages 1741-1759, April.
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    Cited by:

    1. Gioia Arnone & Giovanni Scire’ & Enzo Bivona, 2025. "The (mis)use of cryptocurrencies by criminal organizations: a systematic literature review," Digital Finance, Springer, vol. 7(4), pages 815-851, December.
    2. repec:bcp:journl:v:9:y:2025:i:10:p:1420-1431 is not listed on IDEAS

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