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All Journal International Journal of Artificial Intelligence Research
A. Said, Gamal Abd El-Nasser
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Computer Science & IT Electrical & Electronics Engineering

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Explainable AI-Based Real-Time Hybrid System for Blockchain Anomaly Detection: A Multi-Cryptocurrency Perspective Shabaan, Amira Hamdi; Elkaffa, Saleh Mesbah; A. Said, Gamal Abd El-Nasser; Badawy, Ossama Mohamed
International Journal of Artificial Intelligence Research Vol 9, No 2 (2025): December
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i2.1571

Abstract

This 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.  
Co-Authors Badawy, Ossama Mohamed Elkaffa, Saleh Mesbah Shabaan, Amira Hamdi