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Article type: Research Article
Authors: Wang, Jia | Zhang, Ke | Li, Jingyuan; *
Affiliations: Information Construction and Management Division, Shaanxi Normal University, Xi’an, Shaanxi, China
Correspondence: [*] Corresponding author. Jingyuan Li, Information Construction and Management Division, Shaanxi Normal University, Xi’an 710119, Shaanxi, China. E-mail: [email protected].
Abstract: Awareness of Network Security Situation (abbreviated as NSS for short) technology is in a period of vigorous development recently. NSS technology means network security situational awareness technology. It refers to the technology of collecting, processing, and analyzing various real-time information in the network to understand and evaluate the current network security status. It can not only find network security threats, but also reflect the NSS in the system security metrics, and provide users with targeted security protection measures. Based on data mining methods, this paper analyzed and models perceived threats and security events with data mining algorithms, and improved information security measurement methods based on association analysis. This paper proposed network security information analysis and NSS based on data mining, and analyzed the experimental results of network awareness of NSS information security measurement. The experimental results showed that when the Timer was 8, the accuracy of the awareness of NSS information security measurement method based on data mining can reach 92.89%. The data mining model had the highest accuracy of 93.14% in situation understanding and evaluation of KDDCup-99 dataset. The results showed that the model can accurately predict the NSS. When Timer was 6, the highest accuracy of the model was 92.71%. In general, the NSS prediction mining model based on KDDCup-99 can better understand, evaluate and predict the situation.
Keywords: Network security situation, data mining, information security, situation awareness
DOI: 10.3233/JIFS-233390
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 209-219, 2024
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