Abstract

In this thesis, we consider the problem of Bitcoin address classification and clustering, common in the domains of law enforcement and regulatory compliance. We build a machine learning-based classification framework which is able to attribute a Bitcoin address to one of the predefined classes or to a specific company. We consider five distinct classes for coarse-grained classification: cryptocurrency exchanges, online marketplaces, mining pools, fundraising/charity platforms, and gambling; and 180 companies for fine-grained classification. Classes and the companies were selected so that they represent a broad spectrum of entities and activities within the Bitcoin ecosystem.
This thesis has three main contributions. First, due to the lack of publicly available datasets suitable for testing machine-learning classification algorithms, we create our own labeled dataset consisting of 3M Bitcoin addresses (from 2016-2022), with each Bitcoin address assigned a ready-to-use vector of carefully crafted features. Second, using this dataset, we conduct a comparative analysis of different machine-learning techniques and features for classification. Finally, we develop
two types of classifiers: based on the Boosted tree algorithm and the neural network-based classifier. Both are able to attribute a Bitcoin address to one of the predefined classes/companies. Our binary classification model achieves an F1 score of 76% using the Boosted tree algorithm, while our deep learning model achieves a 90% F1 score for multi-class classification with an accuracy of 92% and 28% higher than related work correspondingly. We achieve 67% accuracy for linking Bitcoin addresses to one of the 180 companies with our deep-learning model.