Publication Type
Journal Article
Version
publishedVersion
Publication Date
12-2023
Abstract
Cryptocurrency has been subject to illicit activities probably more often than traditional financial assets due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all three critical properties of early detection, good interpretability, and versatility for various illicit activities. However, existing solutions cannot meet all these requirements, as most of them heavily rely on deep learning without interpretability and are only available for retrospective analysis of a specific illicit type. To tackle all these challenges, we propose Intention Monitor for early malice detection in Bitcoin, where the on-chain record data for a certain address are much scarcer than other cryptocurrency platforms.We first define asset transfer paths with the Decision Tree based feature Selection and Complement to build different feature sets for different malice types. Then, the Status/Action Proposal module and the Intention-VAE module generate the status, action, intent-snippet, and hidden intent-snippet embedding. With all these modules, our model is highly interpretable and can detect various illegal activities. Moreover, well-designed loss functions further enhance the prediction speed and the model’s interpretability. Extensive experiments on three real-world datasets demonstrate that our proposed algorithm outperforms the state-of-the-art methods. Furthermore, additional case studies justify that our model not only explains existing illicit patterns but also can find new suspicious characters.
Keywords
Cybercrime, Malicious address, Early detection, Intention discovery, Cryptocurrency, Bitcoin
Discipline
Databases and Information Systems | Information Security
Research Areas
Data Science and Engineering
Publication
ACM Transactions on Knowledge Discovery from Data
Volume
18
Issue
3
First Page
1
Last Page
27
ISSN
1556-4681
Identifier
10.1145/3626102
Publisher
Association for Computing Machinery (ACM)
Citation
CHENG, Ling; ZHU, Feida; WANG, Yong; LIANG, Ruicheng; and LIU, Huiwen.
From asset flow to status, action and intention discovery: Early malice detection in cryptocurrency. (2023). ACM Transactions on Knowledge Discovery from Data. 18, (3), 1-27.
Available at: https://ink.library.smu.edu.sg/sis_research/8598
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.1145/3626102