TSEE: A Novel Knowledge Embedding Framework for Cyberspace Security

Knowledge representation models have been extensively studied and they providean important foundation for artificial intelligence. However, the existing knowledge representation models or related knowledge embedding methods mostly aimat static or temporal knowledge, which are not suitable for highly spatio-temporalrelevant knowledge, such as the cyber security knowledge. In this paper, we propose a knowledge embedding framework called TSEE to handle this problem,which builds on the MDATA model to represent and utilize dynamic knowledgefor cyber security. TSEE is composed of knowledge extraction module, knowledgerepresentation module, knowledge embedding module, and situational awareness module. There modules can obtain, transform, and embed cyber securityknowledge from different sources, improving the detection capabilities of variouscomplicated attacks. We conduct experiments on the cyber range for evaluation,and the experimental results validate the higher prediction accuracy and strongerextendability than existing embedding methods. The framework can effectivelyimprove the cyber security defense capabilities in the future.