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
arXiv:2509.07392v1h-index: 1
Originality Incremental advance
AI Analysis
This addresses illicit activity detection in blockchain networks, but it is incremental as it combines existing methods.
The paper tackled anomaly detection in cryptocurrency transactions by proposing a hybrid GCN-GRU model, achieving 0.9470 Accuracy and 0.9807 AUC-ROC on Bitcoin data from 2020-2024.
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.