Computer Network Vulnerability Detection and Semantic Data Analysis Optimization Based on Artificial Intelligence

Huiyan Li and Xinhua Xiao
Additional contact information
Huiyan Li: Hubei Polytechnic University, China
Xinhua Xiao: Hubei Polytechnic University, China

International Journal of Distributed Systems and Technologies (IJDST), 2022, vol. 13, issue 6, 1-10

Abstract: In order to solve the network security vulnerabilities in the process of network information interaction, which affect the integrity and confidentiality of data. The computer network vulnerability detection and semantic data analysis optimization based on artificial intelligence are proposed, and the results show that the final accuracy of the test set is improved to 88.5%, but the false positive rate is as high as 18%. Based on the direct classification model, the code under test is compared with the vulnerability template, the model fuses the two direct classification models and reduces the false positive rate to less than 5% under the condition that the accuracy is basically the same.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDST.308000 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jdst00:v:13:y:2022:i:6:p:1-10

Access Statistics for this article

International Journal of Distributed Systems and Technologies (IJDST) is currently edited by Nik Bessis

More articles in International Journal of Distributed Systems and Technologies (IJDST) from IGI Global
Bibliographic data for series maintained by Journal Editor ().