Optimal Design of Computer Network Security Performance Based on Genetic Algorithm. Issue 3 (April 2021)

• Record Type: Journal Article
• Authors: Chen, Fengfeng
• Is Part Of: Journal of physics. Volume 1852:Issue 3(2021)
• Publication Date: 2021-04
• Subjects: Genetic Algorithm -- Support Vector Machine -- Network Security Performance -- Intrusion Detection; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1852/3/032054
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: With the current implementation of my country's reform and opening up, a large number of advanced concepts have been introduced for domestic science and technology, the purpose is to gradually optimize the security performance of domestic computer network (CN) systems. Network security cannot be guaranteed and the development process of CNs cannot be guaranteed. Therefore, people apply genetic algorithms to the innovation and development of CN security performance and obtain more optimization benefits. CN attack technology is diverse and concealed, which makes it difficult to be detected, seriously endangering CN security and accurately identifying network abnormalities. In order to overcome the shortcomings of traditional network security and the low detection accuracy of anomaly detection technology, a genetic algorithm-based support vector machine-based network security performance optimization method was developed. This article first reviews the research and development of CN security systems at home and abroad, summarizes the main problems currently facing the research field of CN security systems, and introduces the main performance optimization technologies, aiming at the current existing CNs The ubiquitous performance shortcomings and problems of security systems, based on the in-depth analysis of the characteristics of network attacks and intrusions, have carried out a more systematic study of the genetic algorithm theory. The experimental results show that the network anomaly detection accuracy of the least square support vector machine classifier based on genetic algorithm optimization is high and the effect is good, which provides a reliable guarantee for the optimization of network security performance.