Unraveling the Deception of Web3 Phishing Scams
Dynamic Multiperspective Cascade Graph Approach for Ethereum Phishing Detection
Bibliographic Data
| ID | 22108142 |
|---|---|
| Authors | Lejun Zhang (0000-0002-3458-7431, Guangzhou University), Xucan Zhang (0009-0005-8649-5837, Guangzhou University), Siyi Xiao (0000-0002-1071-6497, Yangzhou University), Zexin Li (0009-0006-4862-5642, Guangzhou University), Shen Su (0000-0003-2744-3584, Guangzhou University), Jing Qiu (0000-0003-4202-7802, Guangzhou University), Zhihong Tian (0000-0002-9409-5359, Guangzhou University) |
| Year | 2025 |
| Volume | 12 |
| Issue | 2 |
| Pages | 498-510 |
| Publication date | 2025-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3516144 |
| OpenAlex | W4405934304 |
| Language | EN |
| References cited | 30 |
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
Computer security · Deception · Internet privacy · Malware · Phishing · The Internet · World Wide Web · Computer Science · Internet Traffic Analysis and Secure E-voting · Misinformation and Its Impacts · Psychology · Social Psychology · Spam and Phishing Detection
| Highly cited | No |
|---|