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Issue title: Special Section: Intelligent & fuzzy theory in engineering and science
Guest editors: Teresa Guarda, Isabel Lopes and Álvaro Rocha
Article type: Research Article
Authors: Hua, Tao; * | Li, Laixiang
Affiliations: Experiment Teaching and Network Information Center, Liaocheng University, Liaocheng, China
Correspondence: [*] Corresponding author. Tao Hua, Experiment Teaching and Network Information Center, Liaocheng University, Liaocheng, China. E-mail: huat@lcu.edu.cn.
Abstract: With the rapid development of the Internet, the use of computer networks is becoming more and more frequent. However, in the process of network use, various vulnerabilities and some other factors lead to network security problems occur from time to time. How to make the network provide efficient and rapid services for people and also meet different levels of security issues has become a problem. Based on this, the use of artificial intelligence technology has been studied, and the system detection algorithm based on the concept of artificial intelligence has been proposed. Integrating artificial intelligence into the field of network security can not only improve the overall performance of the network, but also effectively and reliably guarantee the security. Firstly, according to the characteristics of computer network system, an immune detection algorithm is proposed to evaluate the security of the system. Secondly, based on the analysis of computer network security technology, a system detection algorithm based on artificial intelligence concept is further proposed. Then, data mining is carried out based on Prefix, and the intrusion lines and their correlation are analyzed. Finally, the effectiveness of the algorithm is analyzed experimentally. The results show that the proposed algorithm has good accuracy and adaptability, and can play a good supporting role in the security detection of computer network system.
Keywords: Artificial intelligence, computer network, security technology
DOI: 10.3233/JIFS-179184
Journal: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 5, pp. 6021-6028, 2019
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