Mapping Deep Learning to Blockchain Security: A Survey

This survey explores how specific artificial neural networks (such as GNNs, CNNs, RNNs, LSTMs, GANs, Transformers, MLPs, and hybrid models) have been applied to secure blockchain systems. Blockchain technology continues to expand across various fields due to its decentralized, tamper-resistant qualities. However, several concerns have been raised since they are more vulnerable to real-time threats and attacks. Deep learning, a branch of artificial intelligence (AI), presents itself as a promising solution for enhancing blockchain security due to its ability to learn from complex datasets. We categorize current research by neural network type, the application domain of the model, applied blockchain layers, security/privacy, and attack/defense, and identify existing patterns, challenges, and research gaps. Several conclusions include data scarcity issues, latency, and limited deployment across all blockchain layers (such as the consensus layer and hardware layer). After studying and comparing several existing surveys, we provide a unique contribution by explaining deep learning techniques specifically tailored for blockchain security, which enables us to highlight numerous opportunities for future researchers to address current limitations, such as scalability and privacy.