Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions
Fuente:
arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912578174713856 |
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| author | Na, Gyuyeon Park, Minjung Cha, Hyeonjeong Kim, Soyoun Moon, Sunyoung Lee, Sua Choi, Jaeyoung Lee, Hyemin Chai, Sangmi |
| author_facet | Na, Gyuyeon Park, Minjung Cha, Hyeonjeong Kim, Soyoun Moon, Sunyoung Lee, Sua Choi, Jaeyoung Lee, Hyemin Chai, Sangmi |
| contents | Blockchain transaction networks are complex, with evolving temporal patterns and inter-node relationships. To detect illicit activities, we propose a hybrid GCN-GRU model that captures both structural and sequential features. Using real Bitcoin transaction data (2020-2024), our model achieved 0.9470 Accuracy and 0.9807 AUC-ROC, outperforming all baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07392 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions Na, Gyuyeon Park, Minjung Cha, Hyeonjeong Kim, Soyoun Moon, Sunyoung Lee, Sua Choi, Jaeyoung Lee, Hyemin Chai, Sangmi Machine Learning Artificial Intelligence Blockchain transaction networks are complex, with evolving temporal patterns and inter-node relationships. To detect illicit activities, we propose a hybrid GCN-GRU model that captures both structural and sequential features. Using real Bitcoin transaction data (2020-2024), our model achieved 0.9470 Accuracy and 0.9807 AUC-ROC, outperforming all baselines. |
| title | Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.07392 |