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Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions (2509.07392v1)
Published 9 Sep 2025 in cs.LG and cs.AI
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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