# Cascading Machine Learning to Attack Bitcoin Anonymity

arXiv: https://arxiv.org/abs/1910.06560 · HTML: https://mlsanity.com/paper/1910.06560/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics](https://mlsanity.com/paper/2306.06108/) (2023)
- [Detecting Malicious Accounts showing Adversarial Behavior in Permissionless Blockchains](https://mlsanity.com/paper/2101.11915/) (2021)
- [Towards Malicious address identification in Bitcoin](https://mlsanity.com/paper/2112.11721/) (2021)
- [Pattern Analysis of Money Flow in the Bitcoin Blockchain](https://mlsanity.com/paper/2207.07315/) (2022)
- [textscPerseus: Tracing the Masterminds Behind Cryptocurrency Pump-and-Dump Schemes](https://mlsanity.com/paper/2503.01686/) (2025)
- [Identifying Ransomware Actors in the Bitcoin Network](https://mlsanity.com/paper/2108.13807/) (2021)
- [Machine Learning on Blockchain Data: A Systematic Mapping Study](https://mlsanity.com/paper/2403.17081/) (2024)
- [Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain](https://mlsanity.com/paper/2306.07974/) (2023)

## Abstract (arXiv)

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.