首页|Unraveling the Deception of Web3 Phishing Scams: Dynamic Multiperspective Cascade Graph Approach for Ethereum Phishing Detection

Unraveling the Deception of Web3 Phishing Scams: Dynamic Multiperspective Cascade Graph Approach for Ethereum Phishing Detection

扫码查看
原文链接
  • NETL
  • NSTL
  • IEEE
Ethereum, as one of the most active cryptocurrency trading platforms, has garnered significant academic interest due to its transparent and accessible transaction data. In recent years, phishing scams have emerged as a serious criminal activity on Ethereum. Although most studies model Ethereum account transactions as networks and analyze them using traditional machine learning or network representation learning techniques, these approaches often rely solely on the latest static transaction records or use manually designed features while neglecting transaction histories, thus failing to fully capture the dynamic interactions and potential trading patterns between accounts. This article introduces an innovative multiperspective cascaded dynamic graph neural network model named DMPCG, which extracts phishing transaction data from authoritative databases like blockchain explorers to construct transaction network graphs. The model elevates the analysis from the microscopic features of nodes to the macroscopic dynamics of the entire network, integrating the attributes of static snapshot graphs with the evolution of dynamic trading networks, significantly enhancing the accuracy of phishing detection. Experimental results demonstrate that the DMPCG method achieves an impressive precision of 92.6% and an F1-score of 90.9%, outperforming existing baseline models and traditional subgraph sampling techniques.

PhishingBlockchainsFeature extractionElectronic mailData miningReal-time systemsData modelsSpatiotemporal phenomenaResearch and developmentMonitoring

Lejun Zhang、Xucan Zhang、Siyi Xiao、Zexin Li、Shen Su、Jing Qiu、Zhihong Tian

展开 >

Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, China|Research and Development Center for E-Learning, Ministry of Education, Beijing, China|College of Information Engineering, Yangzhou University, Yangzhou, China|Quanzhou Normal University, Quanzhou, China|Pazhou Laboratory, Guangzhou, China

Cyberspace Institute Advanced Technology, Guangzhou University, Guangzhou, China

College of Information Engineering, Yangzhou University, Yangzhou, China

2025

10.1109/TCSS.2024.3516144

IEEE transactions on computational social systems

IEEE transactions on computational social systems

ISSN：
年,卷(期)：2025.12(2)
  • 38