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Bitcoin has been subject to illicit activities more often than probably anyother financial assets, due to the pseudo-anonymous nature of its transactingentities. An ideal detection model is expected to achieve all the threeproperties of (I) early detection, (II) good interpretability, and (III)versatility for various illicit activities. However, existing solutions cannotmeet all these requirements, as most of them heavily rely on deep learningwithout satisfying interpretability and are only available for retrospectiveanalysis of a specific illicit type.  First, we present asset transfer paths, which aim to describe addresses'early characteristics. Next, with a decision tree based strategy for featureselection and segmentation, we split the entire observation period intodifferent segments and encode each as a segment vector. After clustering allthese segment vectors, we get the global status vectors, essentially the basicunit to describe the whole intention. Finally, a hierarchical self-attentionpredictor predicts the label for the given address in real time. A survivalmodule tells the predictor when to stop and proposes the status sequence,namely intention. %  With the type-dependent selection strategy and global status vectors, ourmodel can be applied to detect various illicit activities with stronginterpretability. The well-designed predictor and particular loss functionsstrengthen the model's prediction speed and interpretability one step further.Extensive experiments on three real-world datasets show that our proposedalgorithm outperforms state-of-the-art methods. Besides, additional casestudies justify our model can not only explain existing illicit patterns butcan also find new suspicious characters.

Toward Intention Discovery for Early Malice Detection in Bitcoin