A network attack link detection method based on the knowledge graph. Issue 1 (1st October 2022)

• Record Type: Journal Article
• Authors: Chen, Shanfeng; Hu, Zhao; Yang, Yiyue; Fan, Suchun
• Is Part Of: Journal of physics. Volume 2358:Issue 1(2022)
• Publication Date: 2022-10-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2358/1/012017
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract : Existing network security detection technologies often detect various network security problems separately. There are limitations in these technologies because of the correlation between entity attack detection and attack capital information. To resolve the problem of poor detection performance of existing network attack link detection methods, this paper proposes a network attack link detection method based on the knowledge graph. Specifically, the link knowledge graph is constructed, the embedded calculation of the knowledge graph of the link attack node is established, a knowledge graph audit model of link attack security detection is constructed, and the detection rules of the link attack knowledge graph are established to identify the association between the link network attack entity and the attack asset attribute, and improve the identification and detection of the attack source by associating the knowledge graph. Experimental results show that the proposed method has better link attack association awareness than other methods reported before.