Combining DRL and Graph Neural Networks for Enhanced Network Security

Abstract

Security has always been a major concern with modern systems, especially because network architectures keep growing both in size and complexity. Deep reinforcement learning and graph neural networks emerge as some of the promising tools in enhancing network security to be automated, adaptive, and scalable for the solution in real-time threat detection and mitigation.

This report discusses the integration of DRL with GNNs and their application to network security. Specifically, it looks in detail at how the combined use can help detect vulnerabilities of one's own security protocols, thus paving the way for improvements so as to make these protocols more resistant and resilient against future intrusions. Key challenges considered include scalability, poor quality data, and the need for heavyweight computations, which lead naturally to the review and presentation of proposed solutions. The findings further show how DRL is combined with GNN for different network security applications.