Presented by: Ardeshir Shojaeinasab

Date: Friday, September 20, 2024
Time: 8:00 am
Place: Zoom, link below.

Zoom Details:

Meeting link: https://uvic.zoom.us/j/83097940162?pwd=HqCIyPnTUQgw7AdiIvDSOd3SmTVA7O.1

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Abstract: 

This dissertation examines the challenges of detecting illicit activities in cryptocurrency transactions, with a focus on Bitcoin. It begins by analyzing cryptocurrency mixing services and their obfuscation techniques. The research then provides a comprehensive evaluation framework for these services, conducting an assessment of all available services and academic proposals. Following this, the study introduces a novel framework that uses statistical patterns to identify potential money laundering and clustering cryptocurrency addresses that can reveal real-world identities involved in illicit transactions.

The study then leverages the Elliptic dataset, a graph representation of Bitcoin transactions, to classify illicit activities. While classical machine learning methods struggled with the imbalanced nature of financial fraud data, Graph Neural Networks (GNNs) - specifically Graph Convolutional Networks and Graph Attention Networks - proved more effective. By considering the graph topology and connections between nodes, GNNs significantly reduced false negative rates in detecting illicit transactions.