Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions
By: Gyuyeon Na , Minjung Park , Hyeonjeong Cha and more
Potential Business Impact:
Finds bad guys in Bitcoin money transfers.
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.
Similar Papers
Towards Quantum-Ready Blockchain Fraud Detection via Ensemble Graph Neural Networks
Machine Learning (CS)
Finds fake money transfers on digital ledgers.
A Novel Hybrid Approach Using an Attention-Based Transformer + GRU Model for Predicting Cryptocurrency Prices
Machine Learning (CS)
Predicts Bitcoin and Ethereum prices more accurately.
Fraud detection and risk assessment of online payment transactions on e-commerce platforms based on LLM and GCN frameworks
Computational Engineering, Finance, and Science
Stops online shoppers from being tricked.