Blockchain Phishing Scam Detection via Multi-channel Graph Classification

Dunjie Zhang, Jinyin Chen, Xiaosong Lu

Abstract: With the popularity of blockchain technology, the financial security issues of blockchain transaction networks have become increasingly serious. Phishing scam detectors will protect possible victims and build a healthier blockchain ecosystem. Usually, the existing works define phishing scam detection as a node classification by learning the users’ potential features by graph embedding methods such as random walk or graph neural network (GNN). However, these detectors are suffered from high complexity due to the blockchain transaction networks’ large scale. Addressing this problem, we defined the transaction pattern graphs for users and transformed the phishing scam detection into a graph classification. To extract richer information, we proposed a multi-channel graph classification model (MCGC) with multiple feature extraction channels for GNN. The transaction pattern graphs and MCGC are more able to detect potential phishing scammers by extracting the transaction pattern features of the target users. Extensive experiments on seven benchmark and Ethereum datasets demonstrate that the proposed MCGC can not only achieve state-of-the-art performance in the graph classification task but also achieve effective phishing scam detection based on the target users’ transaction pattern graphs.

External IDs:dblp:conf/blocksys/ZhangCL21