A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin
Published in ACM Transactions on the Web (TWEB), 2022
Recommended citation: Wang, K., Pang, J., Chen, D., Zhao, Y., Huang, D., Chen, C., & Han, W. (2022). "A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin." ACM Transactions on the Web (TWEB). 16(2): 1-29. http://academicpages.github.io/files/tweb_ransomware_analysis.pdf
Large-scale Empirical Analysis of Ransomware Activities: This paper proposes an industry identification method and uses it to analyze ransomware activities in Bitcoin:
- Proposed new Bitcoin address clustering heuristics for peel chain transactions and lock-time transactions.
- Based on address clustering, proposed a user industry identity identification method based on Graph Neural Networks (GNN) to determine the purpose of user activities.
- Analyzed the illegal fund flows of ransomware activities and the activities of victims from an industry perspective.
Recommended citation: Wang, K., Pang, J., Chen, D., Zhao, Y., Huang, D., Chen, C., & Han, W. (2022). “A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin.” ACM Transactions on the Web (TWEB). 16(2): 1-29.