Research on computer network information security based on improved machine learning

Issue title: Fuzzy model for human autonomous computing in extreme surveillance and it’s applications

Guest editors: Varatharajan Ramachandran

Article type: Research Article

Authors: Guangxu, Yu^{; *}

Affiliations: Henan Institute of Economics and Trade, Zhengzhou, China

Correspondence: [*] Corresponding author. Yu Guangxu, Henan Institute of Economics and Trade, Zhengzhou, China. E-mail: [email protected].

Abstract: The 21st century is an era of rapid development of the Internet. Internet technology is widely used in various fields. With the rapid development of network, the importance of network information security is also highlighted. The traditional network information security technology has been difficult to ensure the security of network information. Therefore, we mainly study the application of machine learning feature extraction method in situational awareness system. A feature selection method based on machine learning is proposed to extract situational features.By analyzing whether the background of network information is safe or not, and according to the current research situation at home and abroad and the trend of Internet development, this paper tries out the practical application of machine learning feature extraction method in a certain perception system. Based on the above points, a selection method based on machine learning is proposed to extract situational features. The accuracy and timeliness of situational awareness system detection are seriously affected by the high dimension, noise and redundant features of massive network traffic data.Therefore, it is of great value to further study network intrusion detection technology on the basis of machine learning.

Keywords: Network information security, security potential perception, intrusion security detection, machine learning

DOI: 10.3233/JIFS-189520

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 4, pp. 6889-6900, 2021