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.
翻译:Bitcoin 是一种分散的隐隐隐隐隐隐隐隐隐, 由同行对同行的链链网络提供。 交易比特币网络的各方不需要披露任何个人信息。 但是, 匿名的属性在某种程度上引发了潜在的恶意交易。 事实上, 各种非法活动, 如洗钱、 深色网络交易和比特币网络的赌博等, 现在并不新鲜。 虽然已经开发了大量的工作来识别恶性比特币交易, 但现有工具在很大程度上忽略了比特币地址的行为分析和分类。 在本文中, 我们提议 BAClasser, 这个工具可以自动根据他们的行为对比特币网络的地址进行分类。 技术上, 我们提出以下三种关键设计。 首先, 我们考虑将比特币地址的交易化成一个地址图示结构, 其中我们引入了图表节缩缩缩缩缩缩缩技术和图形结构增强方法, 以描述统一的图形分类。 此外, 我们利用一个图形特征网络来学习每个地址的图形表达方式, 并生成图形嵌嵌入。 最后, 我们将所有图表的图形嵌入BIBE 格式嵌入方式, 将一个地址的缩缩缩缩缩缩缩缩缩缩缩缩缩缩缩成一个图像格式, 。