Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection
Cryptocurrencies have experienced a surge in popularity, paralleled by an increase in phishing scams exploiting their transactional networks. Therefore, detecting anomalous transactions in the complex structure of cryptocurrency transaction data and the imbalance between legitimate and fraudulent da...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/13/7/1298 |
| _version_ | 1797212743722336256 |
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| author | Su-Hwan Choi Seok-Jun Buu |
| author_facet | Su-Hwan Choi Seok-Jun Buu |
| author_sort | Su-Hwan Choi |
| collection | DOAJ |
| description | Cryptocurrencies have experienced a surge in popularity, paralleled by an increase in phishing scams exploiting their transactional networks. Therefore, detecting anomalous transactions in the complex structure of cryptocurrency transaction data and the imbalance between legitimate and fraudulent data is considered a very important task. To this end, we introduce a model specifically designed for scam detection within the Ethereum network, focusing on its capability to process long and complex transaction graphs. Our method, Deep Graph traversal based on Transformer for Scam Detection (DGTSD), employs the DeepWalk algorithm to traverse extensive graph structures and a Transformer-based classifier to analyze intricate node relationships within these graphs. The necessity for such an approach arises from the inherent complexity and vastness of Ethereum transaction data, which traditional techniques struggle to process effectively. DGTSD applies subgraph sampling to manage this complexity, targeting significant portions of the network for detailed analysis. Then, it leverages the multi-head attention mechanism of the Transformer model to effectively learn and analyze complex patterns and relationships within the Ethereum transaction graph to identify fraudulent activity more accurately. Our experiments with other models demonstrate the superiority of this model over traditional methods in performance, with an F1 score of 0.9354. By focusing on the challenging aspects of Ethereum’s transaction network, such as its size and intricate connections, DGTSD presents a robust solution for identifying fraudulent activities, significantly contributing to the enhancement of blockchain security. |
| first_indexed | 2024-04-24T10:47:14Z |
| format | Article |
| id | doaj.art-f470032d964e408da953879fcc940faa |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-04-24T10:47:14Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-f470032d964e408da953879fcc940faa2024-04-12T13:17:19ZengMDPI AGElectronics2079-92922024-03-01137129810.3390/electronics13071298Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam DetectionSu-Hwan Choi0Seok-Jun Buu1Department of Computer Science, Gyeongsang National University, Jinju-si 52828, Republic of KoreaDepartment of Computer Science, Gyeongsang National University, Jinju-si 52828, Republic of KoreaCryptocurrencies have experienced a surge in popularity, paralleled by an increase in phishing scams exploiting their transactional networks. Therefore, detecting anomalous transactions in the complex structure of cryptocurrency transaction data and the imbalance between legitimate and fraudulent data is considered a very important task. To this end, we introduce a model specifically designed for scam detection within the Ethereum network, focusing on its capability to process long and complex transaction graphs. Our method, Deep Graph traversal based on Transformer for Scam Detection (DGTSD), employs the DeepWalk algorithm to traverse extensive graph structures and a Transformer-based classifier to analyze intricate node relationships within these graphs. The necessity for such an approach arises from the inherent complexity and vastness of Ethereum transaction data, which traditional techniques struggle to process effectively. DGTSD applies subgraph sampling to manage this complexity, targeting significant portions of the network for detailed analysis. Then, it leverages the multi-head attention mechanism of the Transformer model to effectively learn and analyze complex patterns and relationships within the Ethereum transaction graph to identify fraudulent activity more accurately. Our experiments with other models demonstrate the superiority of this model over traditional methods in performance, with an F1 score of 0.9354. By focusing on the challenging aspects of Ethereum’s transaction network, such as its size and intricate connections, DGTSD presents a robust solution for identifying fraudulent activities, significantly contributing to the enhancement of blockchain security.https://www.mdpi.com/2079-9292/13/7/1298graph traversinggraph neural networkDeepWalktransformercryptocurrency securityfraud detection |
| spellingShingle | Su-Hwan Choi Seok-Jun Buu Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection Electronics graph traversing graph neural network DeepWalk transformer cryptocurrency security fraud detection |
| title | Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection |
| title_full | Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection |
| title_fullStr | Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection |
| title_full_unstemmed | Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection |
| title_short | Learning to Traverse Cryptocurrency Transaction Graphs Based on Transformer Network for Phishing Scam Detection |
| title_sort | learning to traverse cryptocurrency transaction graphs based on transformer network for phishing scam detection |
| topic | graph traversing graph neural network DeepWalk transformer cryptocurrency security fraud detection |
| url | https://www.mdpi.com/2079-9292/13/7/1298 |
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