• Skip to primary navigation
  • Skip to main content

CINA.

  • About
    • Mission
    • People
  • Research
    • Research
    • Projects
    • RFPs
  • Education
    • Resources
  • Publications
    • Newsletter Archive
    • Director’s Blog
  • News & Events
    • News
    • CINA Director’s Blog
    • Digital Archive
    • Events
    • Work with Us
  • Contact
  • Search Toggle
  • Skip to content

Is there money laundering in cryptocurrency markets?

Summary

The explosive growth in the number of circulating cryptocurrencies has formed one of the largest unregulated markets in the world. Investigators need tools to not only overcome the challenge of calculating the actual volume of illicit activities in the cryptocurrency space, but to detect and disrupt illicit transactions and/or the underlying criminal activities. This project will apply a novel algorithm to transaction data, resulting in a probabilistic machine learning tool for law enforcement to decide on how to best target resources to seek and dismantle money laundering operations in cryptocurrency markets, enhancing law enforcement efforts in the face of new technologies.

 

Problem addressed

The goal of our project is to estimate a lower bound of the extent of money laundering over a large number of cryptocurrencies, and to use our observations to develop automated (probabilistic) tools which serve the operational need of identifying cryptocurrency wallets that are likely involved in illegal activities, so that federal and state law enforcement can detect and disrupt illicit transactions or the underlying criminal activities. Our scope goes beyond pseudo-anonymous cryptocurrencies, as we develop new approaches for correlating transactions in private cryptocurrencies and mixing services.

 

Approach

Our industry partner Blockchain Intelligence Group (BIG) provides us with a set of cryptocurrency addresses that have been labeled as malicious due to some specific activities (i.e. an address that was involved in dark web transactions, an address that was used in a scam, addresses from public trial cases, etc.), which we use to estimate the scale of illicit activity for the top 6-7 cryptocurrencies (which in total control about 90% of the market capital). To detect wallets potentially involved in illicit activity, we conduct a network clustering analysis within all addresses of each cryptocurrency. Using different sensitivity levels for our ML algorithms, we are developing a probabilistic tool that aims to assign a score of malicious activity to new addresses/transactions. Past work has focused on BitCoin, but we are focused on Dash, the second most popular private cryptocurrency in terms of market capital, with the goal to correlate participants who attempt to “mix” their funds.

 

Results

We have completed the analysis of Ethereum and are at the final steps of completing the analysis of two more of the largest cryptocurrencies, Litecoin and Bitcoin cash. In Ethreum we had a ground truth set of about 3000 addresses, which we found to currently control over $900,000,000, and using our probabilistic tool managed to extend the 3000 addresses to a set of over 23,000 addresses. The expanded set only added approximately 25% to the total funds. Our preliminary analysis of Dash indicates that the offered anonymity level is less than thought, and we are currently experimenting with a number of heuristics to concretely connect additional addresses.

 

Anticipated Impact for DHS

Bitcoin and Ethereum ledgers could expose information about transaction sequences in a way that limits the utility of these cryptocurrencies for illicit transactions. We expect that our results will provide DHS/HSE with objective, data-based evidence regarding the extent of cryptocurrency utilization in money laundering operations. This is key information for law enforcement and policymakers operating in federal and state agencies. In particular, ICE is leading efforts to disrupt the potential use of cryptocurrencies for money laundering purposes. In addition, our probabilistic tool could provide an additional approach to identify and disrupt money laundering activities.

 

Research Products:

Publications:

Report: “Dash cryptocurrency deanonymization

Measuring Illicit Activity in DeFi: The Case of Ethereum

Videos:

CINA Research Briefing: Illicit Activities with Crypto Currencies

Topics:

  • Dark web
  • Money laundering
  • Networks

Research Areas:

  • Criminal investigative processes
  • Dynamic patterns of criminal activity

Investigators

  • Foteini Baldimtsi
  • Maurice Kugler
  • Jiasun Li

Related Publications:

  • Dash cryptocurrency deanonymization
  • Measuring Illicit Activity in DeFi: The Case of Ethereum

CINA Now

Events

September 28 @ 12:00 pm

CINA Distinguished Speaker Series with Nick Nikiforakis: “Bridgespotting: How Web3 Attackers Target Web2 Cryptocurrency Users”

All Events

Publications

3D Partial Bloody Fingermark Imaging based on Digital Holography and Transport of Intensity

Published: Aug 17, 2023

Gadgets of Gadgets in Industrial Control Systems: Return Oriented Programming Attacks on PLCs

Published: May 16, 2023
All Publications

News

Advancements in Fingerprint Analysis: Shedding Light on Crime Solving

CINA  |   September 19, 2023  |   Posted In:
  • CINA in the News

2022 CINA Annual Report

CINA  |   September 8, 2023  |   Posted In:
  • Uncategorized
All News

Science and Technology Directorate’s Office of University Programs
George Mason University Logo
Copyright © 2023 All Rights Reserved | CINA Is A Department of Homeland Security Center of Excellence led by George Mason University
  • Facebook
  • Twitter
  • Instagram
  • YouTube