Blockchain Forensics: Investigating the Dynamics and Evolution of Blockchain Crimes

About the Project

This PhD project, "Blockchain Forensics: Investigating the Dynamics and Evolution of Blockchain Crimes," offers a unique opportunity to explore the changing landscape of blockchain crimes, focusing on how technological advancements impact criminal tactics. As the global adoption of blockchain technology continues to rise, so do the challenges associated with tracking and preventing digital crimes such as cryptocurrency theft, fraud, and identity theft. This project seeks to create a real-time, interactive database and analytical dashboard to monitor these crimes, providing insights that benefit researchers, law enforcement, and the industry.

Methodologies include web scraping and text mining to collect relevant data from credible online sources, alongside qualitative interviews with experts in blockchain technology, law enforcement, and cybersecurity. An interactive dashboard will be developed using R Shiny or Power BI, enabling users to visualize and analyze crime patterns in real-time. This research, supervised by leading experts from the University of Manchester and partners, will yield tools and insights valuable for understanding and mitigating blockchain crime. The project will be especially relevant to candidates interested in the intersection of criminology, data science, and blockchain technology, offering practical experience in real-world digital forensics and interdisciplinary collaboration.

Eligibility

Based on the project proposal, the subject experience outside of the standard entry requirements should include:

Undergraduate/master’s degree Areas: Criminology, Data Science, Blockchain Technology, Cybersecurity, Economics or Financial Economics (with exposure to blockchain and cryptocurrency systems)

Professional Experience

• Practical experience in blockchain technology, digital forensics, or crime investigation.
• Familiarity with tools like R, Python, or data visualization and analysis.
• Experience with web scraping, text mining, or handling large unstructured datasets.

Academic Requirements

• Bachelor's (Honours) degree in a cognate subject at 2:1 or above (or overseas equivalent); and
• Master's degree in a relevant subject - with an overall average of 65% or above, a minimum mark of 65% in your dissertation and no mark below 55% (or overseas equivalent)

The project is supported by the University of Manchester's AI Trust and Security initiative. The studentship provides funding for 4 years, beginning in September 2025. It covers tuition fees, and an annual UKRI stipend (2024/25 rate £19,237 per annum).