Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions

Blockchain transaction networks are complex, with evolving temporal patterns and inter - node relationships. To detect illicit activi - ties, we propose a hybrid GCN - GRU model that captu res both structural and sequential features. Using real Bitcoin transaction data (2020 - 2024), our model achieved 0.9470 Accuracy and 0.9807 AUC - ROC, outperform - ing all baselines.