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Published in Proceedings of The 16th International Conference on Wireless Algorithms, Systems, and Applications (WASA), 2021
This paper proposes a Bitcoin user industry identification method, reproducing the main activity trajectories of users across various industries.
Recommended citation: Han, W., Chen, D., Pang, J., Wang, K., Chen, C., Huang, D., & Fan, Z. (2021). "Temporal Networks based Industry Identification for Bitcoin Users." Proceedings of The 16th International Conference on Wireless Algorithms, Systems, and Applications (WASA). 108-120. http://academicpages.github.io/files/wasa_industry_id.pdf
Published in ACM Transactions on the Web (TWEB), 2022
This paper proposes an industry identification method to analyze ransomware activities, utilizing Graph Neural Networks to identify user industry identities and analyze illegal fund flows.
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
Published in 2022 International Conference on Networking and Network Applications (NaNA), 2022
This paper presents a Bitcoin transaction traceability method based on network traffic analysis, achieving high accuracy in linking IP addresses to transactions.
Recommended citation: Huang, D., Chen, H., Chen, C., Wang, K., & Han, W. (2022). "A Traceability Method for Bitcoin Transactions Based on Gateway Network Traffic Analysis." 2022 International Conference on Networking and Network Applications (NaNA). 176-183. (Best Paper Award). http://academicpages.github.io/files/nana_traceability.pdf
Published in Computer Networks (CN), 2024
This paper proposes BFTDiagnosis, an automated security testing framework that injects malicious behaviors to evaluate BFT consensus protocols.
Recommended citation: Wang, J., Zhang, B., Wang, K., Wang, Y., & Han, W. (2024). "BFTDiagnosis: An Automated Security Testing Framework with Malicious Behavior Injection for BFT Protocols." Computer Networks (CN). http://academicpages.github.io/files/bftdiagnosis.pdf
Published in ACM Transactions on the Web (TWEB), 2024
This paper proposes a ransomware address detection method based on Bitcoin transaction relationships, utilizing a cascade feature extraction method and PU-Bagging to handle data imbalance.
Recommended citation: Wang, K., Tong, M. W., Pang, J., Wang, J., & Han, W. (2024). "XRAD: Ransomware Address Detection Method based on Bitcoin Transaction Relationships." ACM Transactions on the Web (TWEB). http://academicpages.github.io/files/xrad.pdf
Published in Journal of Information Science & Engineering (JISE), 2024
This paper expands on traffic analysis methods to provide a robust mechanism for analyzing and tracking Bitcoin transactions within specified network zones.
Recommended citation: Huang, D., Chen, C., Luo, H., Wang, K., & Han, W. (2024). "A Bitcoin Transaction Analyzing and Tracking Mechanism in Specified Network Zone." Journal of Information Science & Engineering (JISE). 40(2): 375-396. http://academicpages.github.io/files/jise_tracking.pdf
Published in Proceedings of the ACM Web Conference 2024 (WWW’24), 2024
This paper introduces a novel method to enhance clustering effectiveness using memory pool data, significantly improving recall rates in Bitcoin de-anonymization.
Recommended citation: Wang, K., Cheng, Y., Tong, M. W., Niu, Z., Pang, J., & Han, W. (2024). "Exploring Unconfirmed Transactions for Effective Bitcoin Address Clustering." Proceedings of the ACM Web Conference 2024 (WWW’24). Singapore. 1880-1891. http://academicpages.github.io/files/www24_clustering.pdf
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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