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Issue title: Special section: Artificial Intelligence driven Big Data Analytics for COVID-19
Guest editors: Xiaolong Li
Article type: Research Article
Authors: Ding, Shijiea; * | Zhang, Zhiweib | Xie, Juna
Affiliations: [a] School of Business, Suzhou University, Suzhou, Anhui, China | [b] School of Information Engineering, Suzhou University, Suzhou, Anhui, China
Correspondence: [*] Corresponding author. Ding, Shijie, School of Busness, Suzhou University, No. 49 Middle Bianhe Road, Suzhou, Anhui, China. E-mail: 709417149@qq.com.
Abstract: With the spread of the COVID-19 epidemic, the government has put forward higher requirements for network security and reliability through the flow of network managers and the release of information. Traditional intrusion detection technology and firewall technology cannot effectively defend against DDoS attacks. This paper analyzes the principles and defects of intrusion detection system and firewall. In this paper, the architecture design of intrusion prevention system which integrates audit and network defense functions is proposed. The system optimizes the detection and analysis component of detecting attack behavior according to the special requirements of attack defense task, and adds the module of attack behavior characteristic analysis and defense strategy generation. The policy execution component uses a special defense engine to execute defense policies, providing the system with deep defense capabilities. Experiments show that the validity and reliability of the key modules in the proposed defense model meet the technical requirements. It has a certain reference value to improve the reliability of network management system under the influence of COVID-19 epidemic situation.
Keywords: Intrusion detection, firewall, COVID-19, DDoS attack, defense strategy generation
DOI: 10.3233/JIFS-189294
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 6, pp. 8961-8969, 2020
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