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
公開日: 2025/9/9
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.