BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain

TL;DR

This paper introduces a novel topological data analysis framework for automatically detecting ransomware-related Bitcoin transactions and addresses, significantly improving detection accuracy over existing heuristic methods.

Contribution

The paper presents the first application of topological data analysis to ransomware detection on Bitcoin, enabling automatic identification of malicious addresses with limited prior data.

Findings

Improved precision and recall in ransomware transaction detection

Effective detection of new ransomware families without prior transaction data

Automated ransomware detection outperforming heuristic approaches

Abstract

Proliferation of cryptocurrencies (e.g., Bitcoin) that allow pseudo-anonymous transactions, has made it easier for ransomware developers to demand ransom by encrypting sensitive user data. The recently revealed strikes of ransomware attacks have already resulted in significant economic losses and societal harm across different sectors, ranging from local governments to health care. Most modern ransomware use Bitcoin for payments. However, although Bitcoin transactions are permanently recorded and publicly available, current approaches for detecting ransomware depend only on a couple of heuristics and/or tedious information gathering steps (e.g., running ransomware to collect ransomware related Bitcoin addresses). To our knowledge, none of the previous approaches have employed advanced data analytics techniques to automatically detect ransomware related transactions and malicious Bitcoin addresses. By capitalizing on the recent advances in topological data analysis, we propose an efficient and tractable data analytics framework to automatically detect new malicious addresses in a ransomware family, given only a limited records of previous transactions. Furthermore, our proposed techniques exhibit high utility to detect the emergence of new ransomware families, that is, ransomware with no previous records of transactions. Using the existing known ransomware data sets, we show that our proposed methodology provides significant improvements in precision and recall for ransomware transaction detection, compared to existing heuristic based approaches, and can be utilized to automate ransomware detection.