Peae-GNN
Phishing Detection on Ethereum via Augmentation Ego-Graph Based on Graph Neural Network
Bibliographic Data
| ID | 22106904 |
|---|---|
| Authors | Hexiang Huang (0009-0008-5878-7770, Yunnan University), Xuan Zhang (0000-0003-2929-2126, Yunnan University), Jishu Wang (0000-0001-5973-2415, Yunnan University), Chen Gao (0000-0003-0576-0060, Yunnan University), Xue Li (0000-0003-2994-8930, Yunnan University), Xue Bin Li (0009-0004-6986-4760, Yunnan University), Rui Zhu (0000-0002-8910-9445, Yunnan University), QiuYing Ma (0000-0002-4202-2027, Yunnan University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 3 |
| Pages | 4326-4339 |
| Publication date | 2024-06-01 |
| Peer Reviewed | Yes |
| 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.2023.3349071 |
| OpenAlex | W4391097141 |
| Language | EN |
| Citations received | 1 |
| References cited | 32 |
Recent years, the successful application of blockchain in cryptocurrency has attracted a lot of attention, but it has also led to a rapid growth of illegal and criminal activities. Phishing scams have become the most serious type of crime in Ethereum. Some existing methods for phishing scams detection have limitations, such as high complexity, poor scalability, and high latency. In this article, we propose a novel framework named phishing detection on Ethereum via augmentation ego-graph based on graph neural network (PEAE-GNN). First, we obtain account labels and transaction records from authoritative websites and extract ego-graphs centered on labeled accounts. Then we propose a feature augmentation strategy based on structure features, transaction features and interaction intensity to augment the node features, so that these features of each ego-graph can be learned. Finally, we present a new graph-level representation, sorting the updated node features in descending order and then taking the mean value of the top n to obtain the graph representation, which can retain key information and reduce the introduction of noise. Extensive experimental results show that PEAE-GNN achieves the best performance on phishing detection tasks. At the same time, our framework has the advantages of lower complexity, better scalability, and higher efficiency, which detects phishing accounts at early stage
Computer security · Database · Database transaction · Exploit · Graph · Machine learning · Phishing · Scalability · The Internet · World Wide Web · Blockchain Technology Applications and Security · Brain Tumor Detection and Classification · Computer Science · Spam and Phishing Detection · Artificial Intelligence · Theoretical Computer Science
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |