From Asset Flow to Status, Action and Intention Discovery: Early Malice
  Detection in Cryptocurrency
Ling Cheng Feida Zhu Yong Wang Ruicheng Liang Huiwen Liu
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.