Fraud detection in blockchain cryptocurrencies

The rise of cryptocurrencies has brought both benefits and challenges, including the increase in cybercriminal activities like money laundering and illegal services. To protect decentralized and anonymous cryptocurrency networks, reliable fraud detection systems are urgently needed. Previous approaches using conventional machine learning and graph-based algorithms have struggled with generalization and robustness. This research focuses on using graph neural network (GNN) architectures, including GCN, GAT, GraphSAGE, GIN, and RGGCN, for cryptocurrency fraud detection. These GNN architectures capture complex dependencies and relationships in Ethereum transactions modeled as graphs. GCN efficiently handles message-passing and aggregation to learn local and global dependencies, while GraphSAGE uses sampling and aggregation for large-scale graphs. GAT incorporates attention mechanisms to focus on relevant nodes, GIN offers flexibility in handling different graph structures, and RGGCN captures both local and global information with residual connections. Extensive experiments were conducted on labeled datasets of fraudulent and genuine transactions to develop accurate and robust fraud detection models. Significant features were extracted from the graph structure, enabling the identification of subtle fraudulent patterns. Overcoming challenges such as imbalanced datasets and scalability is crucial for reliable early detection and prevention of fraud. The findings contribute to enhancing the safety of digital currencies. The goal is to equip financial institutions, regulatory bodies, and cryptocurrency platforms with advanced GNN detection and mitigation tools, using frameworks like GCN, GAT, GraphSAGE, GIN, RGGCN, to safeguard the security of the global cryptocurrency market.