Toward Intention Discovery for Early Malice Detection in Bitcoin
Ling Cheng, Feida Zhu, Yong Wang, Huiwen Liu

TL;DR
This paper introduces an interpretable, real-time intention discovery model for early detection of illicit activities in Bitcoin, combining asset transfer paths, feature segmentation, clustering, and a hierarchical self-attention predictor.
Contribution
It presents a novel, interpretable framework that detects Bitcoin illicit activities early and in real-time, addressing limitations of prior deep learning approaches.
Findings
Outperforms state-of-the-art methods on real-world datasets
Provides interpretable explanations for illicit activity detection
Capable of discovering new suspicious patterns
Abstract
Bitcoin has been subject to illicit activities more often than probably any other financial assets, due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all the three 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 satisfying interpretability and are only available for retrospective analysis 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 feature selection and segmentation, we split the entire observation period into different segments and encode each as a segment vector. After clustering all these segment vectors, we get…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Anomaly Detection Techniques and Applications · Advanced Malware Detection Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings · Feature Selection
