Bitcoin is a kind of decentralized cryptocurrency and widely used in online payment partially for its anonymity mechanism. The anonymity, however, also attracts the usage of cryptocurrency by criminals in ransomware and money laundering, and limits its further application and development. In this paper, we aim to improve Bitcoin's auditability with de-anonymization. Many previous studies have used heuristic clustering or supervised machine learning to analyze the historical transactions for identifying user behaviors. However, heuristic clustering only considers the topological structure of the transaction graph and ignores the transaction attributes. While supervised learning is usually limited by the size of labeled datasets, resulting in an unsatisfactory accuracy. To resolve the above problems, we propose an Adaptive Weighted Attribute Propagation enhanced community detection model, named AWAP, which considers both the transaction's topological structure and the transaction attributes. We first parse the transaction data from the public ledger and construct a bipartite graph to describe correlations between addresses and transactions. Then, we use a five-step feature engineering pipeline to extract Bitcoin address attributes and build an attribute graph. Finally, we design an adaptive weighted attribute propagation algorithm running on the attribute graph to classify the Bitcoin addresses and identify user behaviors. Extensive experiments highlight that AWAP model achieves 12% higher accuracy and 25% higher F-score on average, compared to the state-of-the-art Bitcoin address classifiers and other community detection algorithms. To evaluate the effectiveness of AWAP, we also present two case studies on Bitcoin address classification and Bitcoin trace-ability in ransomware.
All Science Journal Classification (ASJC) codes
- Attribute propagation
- Bitcoin anonymity
- Community detection
- Feature engineering