Affiliations: School of Information Engineering, Mianyang Teachers’ College, Mianyang, Sichuan, P.R. China
Correspondence:
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Corresponding author. Tang Lin, School of Information Engineering, Mianyang Teachers’ College, Mianyang 621000, Sichuan, P.R. China. E-mail: tanglin163wangyi@163.com.
Abstract: Although much less fatal than the Ebola and previous SARS virus epidemics, the current coronavirus outbreak (COVID-19) has spread to more people in more countries in a much shorter time frame. With the rapid development of the Internet of things, it has played an important role to track/monitor transmission movements throughout the population. The technology infrastructure between mobile devices, wearable devices and sensors, smart home device makes it possible to readily deploy solutions to monitor and collect data and perform analysis to ensure policy make intelligent, rapid decisions. This research combines AOL and Support Vector Machine to form the Internet of things cycle through smart home. The parameters of Support Vector Machine model are optimized by ALO algorithm, which shortens the learning time and improves the performance of classifier. Then, the algorithm of ALO is used to optimize the Support Vector Machine intrusion detection method and agent technology, and the intrusion detection model is established. Experimental results show that the combination of these two can effectively reduce the false alarm rate of network intrusion.
Keywords: Support vector machine, intrusion detection, internet of things security, smart home
