Affiliations: [a] School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, China
| [b] Scientific Research Department, Agriculture Science and Technology College, Jilin, China
Correspondence:
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Corresponding author. Wang, Xuesong, Scientific Research Department, Agriculture Science and Technology College, Jilin, China. E-mail: [email protected].
Abstract: With the continuous progress of social science and technology, the development of the Internet of things is growing. With the development of Internet of things, security problems emerge in endlessly. During the period of COVID-19, the Internet of Things have been widely used to fight virus outbreak. However, the most serious security problem of the Internet of things is network intrusion. This paper proposes a balanced quadratic support vector machine information security analysis method for Internet of things. Compared with the traditional support vector machine Internet of things security analysis method, this method has a higher accuracy, and can shorten the detection time, with efficient and powerful characteristics. The method proposed in this paper has certain reference value to the Internet of things network intrusion problem. It provides better security for the Internet of things during the protection period of covid-19.
Keywords: SVM, Balanced binary decision, internet of things security, intrusion detection, COVID-19
