Phishing Scam Detection for Ethereum Based on Community Enhanced Graph Convolutional Networks

Blockchain technology has garnered a lot of interest recently, but it has also become a breeding ground for various network crimes. Cryptocurrency, for example, has suffered losses due to network phishing scams, posing a serious threat to the security of blockchain ecosystem transactions. To create a favorable investment environment, we propose a community-enhanced phishing scam detection model in this paper. We approach network phishing detection as a graph classification task and introduce a network phishing detection graph neural network framework. Firstly, we construct an Ethereum transaction network and extract transaction subgraphs, and corresponding content features from it. Based on this, we propose a community-enhanced graph convolutional network (GCN)-based detection model. It extracts more reasonable node representations in the GCN neighborhoods and explores the advanced semantics of the graph by defining community structure and measuring the similarity of nodes in the community. This distinguishes normal accounts from phishing accounts. Experiments on different large-scale real-data sets of Ethereum consistently demonstrate that our proposed model performs better than related methods.