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arXiv · 2509.07392

Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions

Gyuyeon Na·Minjung Park·Hyeonjeong Cha·Soyoun Kim·Sunyoung Moon·Sua Lee·Jaeyoung Choi·Hyemin Lee·Sangmi Chai
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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.

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Gyuyeon Na, Minjung Park, Hyeonjeong Cha, Soyoun Kim, Sunyoung Moon, Sua Lee, Jaeyoung Choi, Hyemin Lee, Sangmi Chai. 2025-09-09. Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions. https://arxiv.org/abs/2509.07392

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