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(collage of computing, AASTMT, Egypt)(2) Saleh Mesbah Elkaffa
(College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Egypt)(3) Gamal Abd El-Nasser A. Said
(College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Alexandria, Egypt)(4) Ossama Mohamed Badawy
(Port Training Institute, AASTMT, Alexandria,, Egypt)*corresponding author
AbstractThis study achieves a 5% improvement in AUC-ROC and a 2.5% increase in recall compared to state-of-the-art anomaly detection methods in blockchain networks. Blockchain technologies have rapidly evolved, offering transparency and security across decentralized systems. However, detecting anomalies and fraudulent activities remains a significant challenge. This research proposes a unified hybrid framework integrating Graph Neural Networks (GNNs), Transformers, and XGBoost within a federated learning environment for real-time anomaly detection in multi-cryptocurrency blockchain networks. Unlike previous works, this model employs explainable AI (XAI) methods (SHAP and LIME) to enhance interpretability and trust. The framework utilizes PSO-based hyperparameter optimization, reducing convergence time by 20%. Experimental evaluations on benchmark datasets (Elliptic, Bitcoin-OTC, and Ethereum) demonstrate superior performance in precision, recall, and FPR compared to CARE-GNN and GeniePath. The results confirm the proposed model’s scalability, transparency, and real-time efficiency, making it suitable for deployment in high-frequency blockchain monitoring systems.
KeywordsBlockchain; Anomaly Detection; Explainable Artificial Intelligence (XAI); Graph Neural Network (GNN); Transformer; XGBoost; Federated Learning; Particle Swarm Optimization (PSO); Cryptocurrency; Fraud Detection
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DOIhttps://doi.org/10.29099/ijair.v9i2.1571 |
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