Bitcoin, as the most valuable cryptocurrency, has become a significant target for criminal activities, leading to substantial financial losses. To timely detect criminal activities and reduce losses, the accurate detection of illicit addresses is crucial. Current research typically involves extracting features from addresses to classify them as either licit or illicit. However, they neglect the transaction topology information associated with the addresses, thereby compromising the efficiency. In this paper, we focus on improving the utilization of address information and propose DIBA detector, an effective framework designed for the automatic d etection of i llicit B itcoin a ddresses. DIBA first incorporates a transaction graph construction module that constructs per-address transaction graph based on UTXO model, thereby mapping to the transaction topology for each address. Subsequently, a novel hybrid spatiotemporal network is designed to learn graph representation for each per-address transaction graph, generating comprehensive graph embeddings that serve as critical inputs for the final classification model. Experimental results demonstrate that our proposed framework outperforms existing state-of-the-art methods for detecting illicit Bitcoin addresses, achieving precision and F1-score values of 92.77% and 93.58%, respectively. As a side contribution, we construct a dataset comprising over 180,000 addresses, with half labeled as licit and half as illicit. Our dataset is released at https://github.com/dlvlb123/DIBA.
Duan et al. (Tue,) studied this question.