NLP-Augmented Knowledge Graph Embedding for Relationship Prediction in Network Security

In the field of network security, the ability to build an effective knowledge graph is of paramount importance to understand the nature of attacks, recognize potential vulnerabilities, and develop strategies to protect against threats. In traditional graphs, the primary focus is on the node topology, where nodes are often represented by specific identifiers. However, these identifiers are insufficient for revealing deeper connections. Textual descriptions of vulnerabilities and attack methods are rich in technical details and semantic context, which are essential for understanding complex relationships. This paper proposes a novel approach that combines natural language processing (NLP) with knowledge graph reasoning algorithms to improve relationship prediction. The results of the experimental study validate the effectiveness of this method, demonstrating that the integration of NLP significantly enhances the accuracy and performance of the prediction, outperforming traditional graph structure algorithms.