Like any other useful technology, cryptocurrencies are sometimes used for criminal activities. While transactions are recorded on the blockchain, there exists a need for a more rapid and scalable method to detect addresses associated with fraudulent activities. We present RiskSEA, a scalable risk scoring system capable of effectively handling the dynamic nature of large-scale blockchain transaction graphs. The risk scoring system, which we implement for Ethereum, consists of 1. a scalable approach to generating node2vec embedding for entire set of addresses to capture the graph topology 2. transaction-based features to capture the transactional behavioral pattern of an address 3. a classifier model to generate risk score for addresses that combines the node2vec embedding and behavioral features. Efficiently generating node2vec embedding for large scale and dynamically evolving blockchain transaction graphs is challenging, we present two novel approaches for generating node2vec embeddings and effectively scaling it to the entire set of blockchain addresses: 1. node2vec embedding propagation and 2. dynamic node2vec embedding. We present a comprehensive analysis of the proposed approaches. Our experiments show that combining both behavioral and node2vec features boosts the classification performance significantly, and that the dynamic node2vec embeddings perform better than the node2vec propagated embeddings.

翻译：与其他有用技术类似，加密货币有时也被用于犯罪活动。虽然交易记录在区块链上，但仍需要一种更快速、可扩展的方法来检测与欺诈活动相关的地址。我们提出了RiskSEA，这是一个可扩展的风险评分系统，能够有效处理大规模区块链交易图的动态特性。我们为以太坊实现的风险评分系统包含：1. 一种为全部地址生成node2vec嵌入的可扩展方法，以捕捉图拓扑结构；2. 基于交易的特征，以捕捉地址的交易行为模式；3. 一个结合node2vec嵌入与行为特征的分类器模型，用于生成地址风险评分。为大规模且动态演化的区块链交易图高效生成node2vec嵌入具有挑战性，我们提出了两种生成node2vec嵌入并将其有效扩展至全部区块链地址的新方法：1. node2vec嵌入传播；2. 动态node2vec嵌入。我们对所提方法进行了全面分析。实验表明，结合行为特征与node2vec特征能显著提升分类性能，且动态node2vec嵌入的表现优于传播式node2vec嵌入。