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Cryptocurrency has been subject to illicit activities probably more oftenthan traditional financial assets due to the pseudo-anonymous nature of itstransacting entities. An ideal detection model is expected to achieve all threecritical properties of (I) early detection, (II) good interpretability, and(III) versatility for various illicit activities. However, existing solutionscannot meet all these requirements, as most of them heavily rely on deeplearning without interpretability and are only available for retrospectiveanalysis of a specific illicit type. To tackle all these challenges, we proposeIntention-Monitor for early malice detection in Bitcoin (BTC), where theon-chain record data for a certain address are much scarcer than othercryptocurrency platforms. We first define asset transfer paths with theDecision-Tree based feature Selection and Complement (DT-SC) to build differentfeature sets for different malice types. Then, the Status/Action ProposalModule (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 andmodel's interpretability. Extensive experiments on three real-world datasetsdemonstrate that our proposed algorithm outperforms the state-of-the-artmethods. Furthermore, additional case studies justify our model can not onlyexplain existing illicit patterns but can also find new suspicious characters.

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