From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrency

• URL: http://arxiv.org/abs/2309.15133v1
• Date: Tue, 26 Sep 2023 07:12:59 GMT
• Title: From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrency
• Authors: Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu
• Abstract summary: An ideal detection model is expected to achieve all three critical properties of (I) early detection, (II) good interpretability, and (III) versatility for various illicit activities. We propose Intention-Monitor for early malice detection in Bitcoin (BTC), where the on-chain record data for a certain address are much scarcer than other cryptocurrency platforms. Our model is highly interpretable and can detect various illegal activities.
• Score: 9.878712887719978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (I) early detection, (II) good interpretability, and (III) 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 (BTC), 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 (DT-SC) to build different feature sets for different malice types. Then, the Status/Action Proposal Module (S/A-PM) 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 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 our model can not only explain existing illicit patterns but can also find new suspicious characters.

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