Cascading Machine Learning to Attack Bitcoin Anonymity

TL;DR

This paper presents a cascading machine learning approach that enhances Bitcoin entity classification accuracy using minimal blockchain features, aiding in the detection of illicit activities.

Contribution

It introduces a novel cascading machine learning method that improves Bitcoin entity classification with limited features, outperforming baseline models.

Findings

High classification precision close to 1.0 for each class

Significantly higher accuracy than baseline models

Effective for Bitcoin entity characterization

Abstract

Bitcoin is a decentralized, pseudonymous cryptocurrency that is one of the most used digital assets to date. Its unregulated nature and inherent anonymity of users have led to a dramatic increase in its use for illicit activities. This calls for the development of novel methods capable of characterizing different entities in the Bitcoin network. In this paper, a method to attack Bitcoin anonymity is presented, leveraging a novel cascading machine learning approach that requires only a few features directly extracted from Bitcoin blockchain data. Cascading, used to enrich entities information with data from previous classifications, led to considerably improved multi-class classification performance with excellent values of Precision close to 1.0 for each considered class. Final models were implemented and compared using different machine learning models and showed significantly higher accuracy compared to their baseline implementation. Our approach can contribute to the development of effective tools for Bitcoin entity characterization, which may assist in uncovering illegal activities.