Abstract
Blockchain, as an emerging technology, has vulnerabilities that make the blockchain ecosystem rife with many criminal activities. However, existing technologies of phishing fraud detection heavily rely on shallow machine learning, leading to low detection precision. To solve this problem, in this paper, we construct a graph classification network model TransDetectionNet. Particularly, we propose a node embedding algorithm named Edge-sampling To Node Vector (Esmp2NVec) that can effectively extract the features hiding in the directed transaction network. Then, we use graph convolutional neural networks (GraphSAGE and GCN) to learn the topological space structure between nodes for further extraction of node features, where the nodes represent Ethereum accounts. To evaluate the method, a series of transaction data from the real Ethereum system is leveraged to train the graph classification model, and several experiments are designed to measure the phishing accounts identification performance comparison between our method and the other related works. The final results of those experiments show that our method can effectively detect phishing accounts from the Ethereum system.
This work was supported in part by the NSF of China under Grants 61832012, 61771289 and 61902202, and the Piloting Fundamental Research Program for the Integration of Scientific Research, Education and Industry of Qilu University of Technology (Shandong Academy of Sciences) under Grant 2022XD001.
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Duan, X., Yan, B., Dong, A., Zhang, L., Yu, J. (2022). Phishing Frauds Detection Based on Graph Neural Network on Ethereum. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_29
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