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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 4, 2023.
Abstract: In a cloud environment, software defined security services are highly vulnerable to malicious virus attacks. In response to software security issues, this project plans to use machine learning technology to achieve automated detection of software security services in a cloud environment. Firstly, study the intrusion characteristics of software defined security services in cloud environments based on piecewise sample regression, and establish their statistical feature quantities. Then, using the method of decision statistical analysis, achieve its fixed identification. Finally, the intrusion characteristics of software defined security services in the cloud environment are studied and compared with the data in the cloud environment to obtain its power spectral density. On this basis, machine learning methods are used to extract features from software security services in the cloud environment, in order to achieve the goal of automatic extraction and optimization of software security services in the cloud environment. Through simulation experiments, the credibility of the proposed algorithm for software defined security services in the cloud environment was verified, and the attack characteristics of software defined security services in the cloud environment were effectively patched.
Xingjie Huang, Jing Li, Jinmeng Zhao, Beibei Su, Zixian Dong and Jing Zhang, “Research on Automatic Intrusion Detection Method of Software-Defined Security Services in Cloud Environment” International Journal of Advanced Computer Science and Applications(IJACSA), 14(4), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140406
@article{Huang2023,
title = {Research on Automatic Intrusion Detection Method of Software-Defined Security Services in Cloud Environment},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140406},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140406},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {4},
author = {Xingjie Huang and Jing Li and Jinmeng Zhao and Beibei Su and Zixian Dong and Jing Zhang}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.