Ransomware Payments in the Bitcoin Ecosystem
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Abstract (arXiv)
Ransomware can prevent a user from accessing a device and its files until a ransom is paid to the attacker, most frequently in Bitcoin. With over 500 known ransomware families, it has become one of the dominant cybercrime threats for law enforcement, security professionals and the public. However, a more comprehensive, evidence-based picture on the global direct financial impact of ransomware attacks is still missing. In this paper, we present a data-driven method for identifying and gathering information on Bitcoin transactions related to illicit activity based on footprints left on the public Bitcoin blockchain. We implement this method on-top-of the GraphSense open-source platform and apply it to empirically analyze transactions related to 35 ransomware families. We estimate the lower bound direct financial impact of each ransomware family and find that, from 2013 to mid-2017, the market for ransomware payments has a minimum worth of USD 12,768,536 (22,967.54 BTC). We also find that the market is highly skewed with only a few number of players responsible for the majority of the payments. Based on these research findings, policy-makers and law enforcement agencies can use the statistics provided to understand the size of the illicit market and make informed decisions on how best to address the threat.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Apr 2018.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 4; 2024: 2; 2025: 1.
11 papers.
- 2022BABD: A Bitcoin Address Behavior Dataset for Pattern Analysiscited by 5
- 2023Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrencycited by 5
- 2024Bitcoin Research with a Transaction Graph Datasetcited by 3
- 2021Identifying Ransomware Actors in the Bitcoin Networkcited by 1
- 2023From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrencycited by 1
- 2023New Online Communities: Graph Deep Learning on Anonymous Voting Networks to Identify Sybils in Polycentric Governancecited by 1
- 2022On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniquescited by 0
- 2022Toward Intention Discovery for Early Malice Detection in Bitcoincited by 0
- 2023Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchaincited by 0
- 2024Clean Up the Mess: Addressing Data Pollution in Cryptocurrency Abuse Reporting Servicescited by 0
- 2025Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approachcited by 0
- 2025Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach12 Feb 2025
- 2024Bitcoin Research with a Transaction Graph Dataset15 Nov 2024
- 2024Clean Up the Mess: Addressing Data Pollution in Cryptocurrency Abuse Reporting Services28 Oct 2024
- 2023New Online Communities: Graph Deep Learning on Anonymous Voting Networks to Identify Sybils in Polycentric Governance25 Nov 2023
- 2023From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrency26 Sep 2023
- 2023Chainlet Orbits: Topological Address Embedding for the Bitcoin Blockchain18 May 2023
- 2023Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency13 Jan 2023
- 2022Toward Intention Discovery for Early Malice Detection in Bitcoin24 Sep 2022
- 2022On the impact of dataset size and class imbalance in evaluating machine-learning-based windows malware detection techniques13 Jun 2022
- 2022BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis10 Apr 2022
- 2021Identifying Ransomware Actors in the Bitcoin Network28 Aug 2021