Novel Graph Neural Network for Real-Time Blockchain Anomaly Detection with Smart Contract Support

Blockchain networks, which process over 400,000 daily Bitcoin transactions with an estimated value of approximately USD 50 billion, present considerable challenges for fraud detection due to their scale, complexity, and evolving transaction patterns. Conventional rule-based detection systems are inadequate in addressing such dynamic environments. This study introduces a novel multi-modal machine learning framework that integrates Graph Convolutional Networks (GCNs) with attention mechanisms and temporal encoding to enable real-time anomaly detection. The proposed approach leverages transactional attributes, network topology, temporal dynamics, and behavioral features to identify complex fraudulent activities, including money laundering and ransomware-related payments. Experimental evaluation on the Elliptic Bitcoin dataset and synthetic benchmarks demonstrates superior performance, achieving an F1-score of 0.847, precision of 0.863, recall of 0.832, and ROC-AUC of 0.923. Furthermore, a gas-optimized Solidity smart contract is deployed to ensure immutable on-chain anomaly logging, facilitating regulatory compliance. The framework supports processing throughput exceeding 5,000 transactions per second with GPU acceleration, delivering a 15% improvement in efficiency compared to state-of-the-art methods.