Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions

Gyuyeon Na, Minjung Park, Hyeonjeong Cha, Soyoun Kim, Sunyoung Moon +4 more
9/9/2025
cs.LGcs.AI

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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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Cite this paper

@article{na2025hybrid,
  title  = {Hybrid GCN-GRU Model for Anomaly Detection in Cryptocurrency Transactions},
  author = {Gyuyeon Na and Minjung Park and Hyeonjeong Cha and Soyoun Kim and Sunyoung Moon and Sua Lee and Jaeyoung Choi and Hyemin Lee and Sangmi Chai},
  year   = {2025},
  eprint = {2509.07392},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.07392v1}
}

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