  Bitcoin is one of the decentralized cryptocurrencies powered by a
peer-to-peer blockchain network. Parties who trade in the bitcoin network are
not required to disclose any personal information. Such property of anonymity,
however, precipitates potential malicious transactions to a certain extent.
Indeed, various illegal activities such as money laundering, dark network
trading, and gambling in the bitcoin network are nothing new now. While a
proliferation of work has been developed to identify malicious bitcoin
transactions, the behavior analysis and classification of bitcoin addresses are
largely overlooked by existing tools. In this paper, we propose BAClassifier, a
tool that can automatically classify bitcoin addresses based on their
behaviors. Technically, we come up with the following three key designs. First,
we consider casting the transactions of the bitcoin address into an address
graph structure, of which we introduce a graph node compression technique and a
graph structure augmentation method to characterize a unified graph
representation. Furthermore, we leverage a graph feature network to learn the
graph representations of each address and generate the graph embeddings.
Finally, we aggregate all graph embeddings of an address into the address-level
representation, and engage in a classification model to give the address
behavior classification. As a side contribution, we construct and release a
large-scale annotated dataset that consists of over 2 million real-world
bitcoin addresses and concerns 4 types of address behaviors. Experimental
results demonstrate that our proposed framework outperforms state-of-the-art
bitcoin address classifiers and existing classification models, where the
precision and F1-score are 96% and 95%, respectively. Our implementation and
dataset are released, hoping to inspire others.
