Detecting Anomalies in Blockchain Transactions Using Spatial-Temporal Graph Neural Networks

Hanan Al-Harbi

doi:10.62177/amit.v1i1.200

Authors

Hanan Al-Harbi King Saud University

DOI:

https://doi.org/10.62177/amit.v1i1.200

Keywords:

Blockchain, Anomaly Detection, Graph Neural Networks, Spatial-Temporal Analysis, Fraud Detection, Transaction Networks, Decentralized Finance

Abstract

Blockchain networks have become a cornerstone of decentralized finance and digital asset management, yet they remain susceptible to fraudulent activities, money laundering, and illicit financial transactions. Traditional anomaly detection methods, including rule-based systems and supervised machine learning models, often struggle to generalize across evolving blockchain transaction patterns due to their reliance on static heuristics and manually engineered features. Graph-based learning techniques offer a more robust approach by leveraging the inherent structure of blockchain transactions, where wallets and transactions form a dynamic graph.
This study proposes a novel Spatial-Temporal Graph Neural Network (STGNN)-based anomaly detection framework for blockchain transactions. By modeling transaction flows as evolving graphs, the proposed system captures both spatial dependencies between wallets and temporal patterns in transaction sequences. The framework employs Graph Convolutional Networks (GCN) or Graph Attention Networks (GAT) to extract spatial representations, while Gated Recurrent Units (GRU) or Temporal Convolutional Networks (TCN) model the time-dependent evolution of transaction behaviors. The fusion of these spatial-temporal features enables the detection of anomalous transactions that deviate from expected network behaviors.
Experimental evaluations on real-world blockchain datasets demonstrate that the STGNN-based model achieves higher detection accuracy, lower false positive rates, and better adaptability than traditional fraud detection techniques. The study further explores the system's scalability and generalization across different blockchain networks, revealing its potential for real-time monitoring of illicit financial activities. These findings highlight the effectiveness of graph-based deep learning models in strengthening blockchain security and provide a foundation for future research in decentralized fraud detection, anti-money laundering (AML) compliance, and intelligent financial surveillance.

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