Kai Wang

Kai Wang

Publications

Exploring Unconfirmed Transactions for Effective Bitcoin Address Clustering

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

A Bitcoin Transaction Analyzing and Tracking Mechanism in Specified Network Zone

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

XRAD: Ransomware Address Detection Method based on Bitcoin Transaction Relationships

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

BFTDiagnosis: An Automated Security Testing Framework with Malicious Behavior Injection for BFT Protocols

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

A Traceability Method for Bitcoin Transactions Based on Gateway Network Traffic Analysis

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

A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin

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

Temporal Networks based Industry Identification for Bitcoin Users

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