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The Poorest Man in Babylon: A Longitudinal Study of Cryptocurrency Investment Scams

  • Stony Brook University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Governments and regulatory bodies have recognized investment scams as a prevalent form of cryptocurrency fraud. These scams typically use professional-looking websites to lure unsuspecting victims with promises of unrealistically high returns. In this paper, we introduce Crimson, a distributed system designed to continuously detect cryptocurrency investment scam websites as they are created in the wild. During the first 8 months of 2024, Crimson processed approximately 6 billion domain names and classified 43, 572 unique cryptocurrency investment scam websites in real-time. Beyond detection, we provide insights into the design and infrastructure of these websites that can help users recognize scam patterns and assist hosting providers in detecting and blocking such sites. Furthermore, we investigate the inclusion of our detected scam websites in block-lists used by popular web browsers and applications, finding that the vast majority of these websites were absent. On the financial side, by analyzing the transactions incoming to scammer wallets on 6.7% of the sites detected by Crimson, we observe an estimated lower bound of 2.04M USD in losses due to cryptocurrency investment scams.

Original languageEnglish
Title of host publicationWWW 2025 - Proceedings of the ACM Web Conference
PublisherAssociation for Computing Machinery, Inc
Pages1034-1045
Number of pages12
ISBN (Electronic)9798400712746
DOIs
StatePublished - Apr 28 2025
Event34th ACM Web Conference, WWW 2025 - Sydney, Australia
Duration: Apr 28 2025May 2 2025

Publication series

NameWWW 2025 - Proceedings of the ACM Web Conference

Conference

Conference34th ACM Web Conference, WWW 2025
Country/TerritoryAustralia
CitySydney
Period04/28/2505/2/25

Keywords

  • Blockchain
  • Cryptocurrency
  • Financial Loss
  • LLM
  • Scam

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