Exploring the Use of Graph Neural Networks for Blockchain Transaction Analysis and Fraud Detection

Abstract

The digital system is increasing day by day while various organizations are facing problems during transactions and false activities. This research is investigating fraud detection in blockchain transactions- data used to focus on Ethereum_network. To implement the layers of Graph-Convolutional Networks (GCNs) that remain in the study, they convert blockchain transactional data into a graph structure with nodes representing addresses and edges representing transactions. The methodology includes data collection with preprocessing and graph representation in the implementation of GCN layers to analyze and detect deceitful activities. The outcomes illustration of the GNN model achieves a high accuracy score and precision with recall and F1-score. The analyses effectively identify fraudulent transactions while minimizing false positives. This work demonstrates the probability of GNNs to enhance fraud detection in blockchain systems and recommends future research directions convoluted in real-time data integration and advanced neural-network architectures toward advancing the toughness and effectiveness of fraud-detection mechanisms in trendy decentralized financial ecosystems.