{"ID":2872875,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.07392","arxiv_id":"2509.07392","title":"Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2509.07392","url_pdf":"https://arxiv.org/pdf/2509.07392v1","authors":"[\"Gyuyeon Na\",\"Minjung Park\",\"Hyeonjeong Cha\",\"Soyoun Kim\",\"Sunyoung Moon\",\"Sua Lee\",\"Jaeyoung Choi\",\"Hyemin Lee\",\"Sangmi Chai\"]","published":"2025-09-09T05:14:26Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}