On the Economic Significance of Ransomware Campaigns: A Bitcoin Transactions Perspective
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Abstract (arXiv)
Bitcoin cryptocurrency system enables users to transact securely and pseudo-anonymously by using an arbitrary number of aliases (Bitcoin addresses). Cybercriminals exploit these characteristics to commit immutable and presumably untraceable monetary fraud, especially via ransomware; a type of malware that encrypts files of the infected system and demands ransom for decryption. In this paper, we present our comprehensive study on all recent ransomware and report the economic impact of such ransomware from the Bitcoin payment perspective. We also present a lightweight framework to identify, collect, and analyze Bitcoin addresses managed by the same user or group of users (cybercriminals, in this case), which includes a novel approach for classifying a payment as ransom. To verify the correctness of our framework, we compared our findings on CryptoLocker ransomware with the results presented in the literature. Our results align with the results found in the previous works except for the final valuation in USD. The reason for this discrepancy is that we used the average Bitcoin price on the day of each ransom payment whereas the authors of the previous studies used the Bitcoin price on the day of their evaluation. Furthermore, for each investigated ransomware, we provide a holistic view of its genesis, development, the process of infection and execution, and characteristic of ransom demands. Finally, we also release our dataset that contains a detailed transaction history of all the Bitcoin addresses we identified for each ransomware.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Apr 2018.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2.
7 papers.
- 2022BABD: A Bitcoin Address Behavior Dataset for Pattern Analysiscited 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
- 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
- 2024Bitcoin Research with a Transaction Graph Dataset15 Nov 2024
- 2024Clean Up the Mess: Addressing Data Pollution in Cryptocurrency Abuse Reporting Services28 Oct 2024
- 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
- 2022Toward Intention Discovery for Early Malice Detection in Bitcoin24 Sep 2022
- 2022BABD: A Bitcoin Address Behavior Dataset for Pattern Analysis10 Apr 2022
- 2021Identifying Ransomware Actors in the Bitcoin Network28 Aug 2021