CT-GCN: a phishing identification model for blockchain cryptocurrency transactions

【Author】 Fu, Bingxue; Yu, Xing; Feng, Tao

【Source】INTERNATIONAL JOURNAL OF INFORMATION SECURITY

【影响因子】2.427

【Abstract】With the widespread application of blockchain technology, the cyberspace security issue of phishing has also appeared in the emerging blockchain cryptocurrency ecosystem. Because phishing fraud in cryptocurrency transactions has its own unique characteristics compared to traditional phishing, many existing phishing detection algorithms are not usable. Therefore, based on graph convolutional networks, we have researched and built a high-performance model for detecting blockchain cryptocurrency phishing fraud. Our model divides the constructed blockchain cryptocurrency transaction graph into "Sender" and "Receiver" graphs, according to the sending and receiving directions. Then, the edge features in the graph are transferred. Finally, we use a double-layer graph convolution network for feature learning and send it to the classifier for fraud detection. After completing training on the actual dataset collected from Ethereum, the model achieved an accuracy of 88.02% and an F1 score of 88.14% on the test data, which had a better performance than that of the other models. Our model provides a new concept for the detection of phishing scams in blockchain cryptocurrency networks.

【Keywords】Blockchain; Ethereum; Phishing detection; Graph embedding; Transaction networks

【发表时间】

【收录时间】2022-08-28

【文献类型】实验仿真

【主题类别】

区块链治理-市场治理-欺诈犯罪