Cybersecurity threat perception technology based on knowledge graph

Sali, A, Al-Jumaily, A, Jiménez, VPG and Al-Jumeily, D (2023) Cybersecurity threat perception technology based on knowledge graph. Journal of Autonomous Intelligence, 6 (3).

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Abstract

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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions:	Computer Science and Mathematics
Publisher:	Frontier Scientific Publishing
Date of acceptance:	17 July 2023
Date of first compliant Open Access:	21 November 2023
Date Deposited:	21 Nov 2023 17:06
Last Modified:	21 Nov 2023 17:15
DOI or ID number:	10.32629/jai.v6i3.882
URI:	https://ljmu-9.eprints-hosting.org/id/eprint/21918

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