Advancing Cybersecurity: Graph Neural Networks in Threat Intelligence Knowledge Graphs
L. H. Li, Feng Qiang, Li Ma · 2024
This paper explores the escalating complexity of network security threats and the critical need for sophisticated analytical tools to understand and combat these threats effectively. We highlight the efficacy of knowledge graphs in elucidating the complex relationships between various network threats and related entities such as threat actors, attack techniques, and vulnerabilities. Traditional machine learning methods often struggle to grasp the intricate dependencies within these graphs, prompting the adoption of Graph Neural Networks (GNNs). GNNs stand out for their ability to learn representations of graph nodes and edges, capturing and propagating relational information to unearth hidden patterns and facilitate advanced threat analysis. We discuss the advantages of GNNs, including their capacity to integrate diverse data types, handle large-scale knowledge graphs, and reveal critical insights that aid in predicting and mitigating network security threats. Through leveraging GNNs' structural and relational analysis capabilities, this paper demonstrates how organizations can enhance their threat intelligence and develop more robust defense mechanisms against the evolving landscape of network security threats.