Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions
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Abstract (arXiv)
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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References · 10
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
- 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 182
- 2023Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensicscited by 19
- 2016Anomaly Detection in Bitcoin Network Using Unsupervised Learning Methodscited by 11
- 2024Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysiscited by 4
- 2017Transaction Fraud Detection Using GRU-centered Sandwich-structured Modelcited by 3
- 2025Topological Analysis of Mixer Activities in the Bitcoin Networkcited by 3
- 2025SoK: A Survey of Mixing Techniques and Mixers for Cryptocurrenciescited by 2
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