Abstract
Wireless data networks are vulnerable to Trojans, viruses, malware and other attacks, resulting in important data being leaked and tampered with. Therefore, a quantitative evaluation method of wireless data network security situation based on machine learning is proposed. Based on the principles of independence, completeness and scientificity, network security situation assessment indicators are selected, combined with CVSS scoring standards, and network security situation assessment indicators are quantitatively processed to obtain time series of wireless data networks. Based on machine learning and support vector machine algorithm, the process of security situation quantification is established and the parameters of support vector machine algorithm are optimized. Experimental results show that the minimum delay of the proposed method is 4 s, the result is consistent with the actual result, and the minimum error is 5.6%, which fully proves the effectiveness of the proposed method.
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Xu, J. (2023). Machine Learning Based Method for Quantifying the Security Situation of Wireless Data Networks. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_27
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DOI: https://doi.org/10.1007/978-3-031-20099-1_27
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