Phishing Scams Detection in Ethereum Transaction Network 论文

摘要

Blockchain has attracted an increasing amount of researches, and there are lots of refreshing implementations in different fields. Cryptocurrency as its representative implementation, suffers the economic loss due to phishing scams. In our work, accounts and transactions are treated as nodes and edges, thus detection of phishing accounts can be modeled as a node classification problem. Correspondingly, we propose a detecting method based on Graph Convolutional Network and autoencoder to precisely distinguish phishing accounts. Experiments on different large-scale real-world datasets from Ethereum show that our proposed model consistently performs promising results compared with related methods.