Cybersecurity threat perception technology based on knowledge graph

A. Sali, Abdulmajeed Al-Jumaily, Víctor P. Gil Jiménez, Dhiya Al-Jumeily


The issue of complex sources, difficult to understand and share security threat intelligence, this paper realizes deep learning of threat intelligence features based on Restricted Boltzmann Machine, which graphs the original threat intelligence features from high dimensional space to low dimensional space layer by layer, and constructs the cyberspace security threat knowledge graphs. The deep learning used to build a multi-level and structured knowledge graph of cyberspace security threats can reflect the structural characteristics of the knowledge graph, making the graph have a lower dimension and a higher level of abstraction. The experiment verifies the feasibility of constructing the cyberspace security threat knowledge graph, and verifies the security threat perception method based on the knowledge graph is more suitable for the perception of high-intensity security threats by comparing with traditional threat detection methods.


knowledge graphs; threat intelligence; Restricted Boltzmann Machine; security threat perception; threat detection

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