Fraud Detection in Blockchain Transactions Using Graph-Based Deep Learning

Abstract

A thorough analysis of graph-based deep learning techniques for blockchain transaction fraud detection is presented in this paper. Blockchain networks' decentralized and pseudonymous structure creates special difficulties that make conventional fraud detection strategies useless, calling for sophisticated methods that can examine intricate transactional relationships. In comparison to traditional machine learning techniques, we perform a systematic evaluation of graph neural network (GNN) architectures, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), showing their superior ability to identify complex fraud patterns like money laundering, Ponzi schemes, and phishing attacks. Our work demonstrates how these models take advantage of the intrinsic graph structure of blockchain transactions by using network topology, node features, and edge attributes to accurately identify anomalous activity. The report also looks at important issues facing the industry, such as the need for cross-chain fraud detection capabilities, interpretability requirements for regulatory compliance, and scalability constraints for real-time analysis. We demonstrate through experimental validation on real-world blockchain datasets that attention-based GNNs maintain computational efficiency while achieving notable gains in detection accuracy (F1-score of 0.91). Promising research directions are outlined in the paper's conclusion, including the creation of explainable AI frameworks for forensic investigations and the incorporation of temporal graph networks for dynamic fraud pattern recognition. These developments establish graph-based deep learning as a game-changing strategy for protecting blockchain ecosystems from changing financial crimes.