A Cybersecurity Knowledge Graph Completion Method for Scalable Scenarios

Cybersecurity knowledge graph can well organize and manage cybersecurity data. However, there are still some challenges in the utilization of cybersecurity knowledge graph. Due to the limited cognitive ability and cyberspace exploration ability of human beings, the data in cybersecurity knowledge graph is incomplete. With the continuous changes of cyberspace, there are many unseen entities in the newly added data, and it is difficult to use this kind of data. In order to realize knowledge graph completion in scalable scenarios, we propose a knowledge graph completion method, which uses meta-learning to transfer knowledge from seen entities to unseen entities. It also utilizes a new scoring function to model the relationships between entities from multiple perspectives such as spatial rotation and angle transformation. For improving the robust expression of samples, it uses the correlation matrix and multi-head attention mechanism to explore the relationships between samples. To mitigate the catastrophic forgetting problem, a new self-distillation algorithm is designed to enhance the robustness of the trained model. We construct knowledge graph based on cybersecurity data, and conduct knowledge graph completion experiments. The experiments show that our method is effective in dealing with the problem of cybersecurity knowledge graph completion in scalable scenarios.