A Cybersecurity Knowledge Graph Completion Method Based on Ensemble Learning and Adversarial Training

Abstract

The application of cybersecurity knowledge graphs is attracting increasing attention. However, many cybersecurity knowledge graphs are incomplete due to the sparsity of cybersecurity knowledge. Existing knowledge graph completion methods do not perform well in domain knowledge, and they are not robust enough relative to noise data. To address these challenges, in this paper we develop a new knowledge graph completion method called CSEA based on ensemble learning and adversarial training. Specifically, we integrate a variety of projection and rotation operations to model the relationships between entities, and use angular information to distinguish entities. A cooperative adversarial training method is designed to enhance the generalization and robustness of the model. We combine the method of generating perturbations for the embedding layers with the self-adversarial training method. The UCB (upper confidence bound) multi-armed bandit method is used to select the perturbations of the embedding layer. This achieves a balance between perturbation diversity and maximum loss. To this end, we build a cybersecurity knowledge graph based on the CVE, CWE, and CAPEC cybersecurity databases. Our experimental results demonstrate the superiority of our proposed model for completing cybersecurity knowledge graphs.