Security threat model under internet of things using deep learning and edge analysis of cyberspace governance

Zhi Li (), Yuemeng Ge (), Jieying Guo (), Mengyao Chen () and Junwei Wang ()
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Zhi Li: NingboTech University
Yuemeng Ge: City University of Macau
Jieying Guo: NingboTech University
Mengyao Chen: NingboTech University
Junwei Wang: NingboTech University

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 3, No 20, 1164-1176

Abstract: Abstract Under the background of information age, it is essential to cope with network security problems, ensure the popularization of Internet of Things (IoT) technology based on the Internet, and guarantee the information security, life security, and property security of all countries and individuals. Therefore, the principle and advantages of deep learning (DL) technology is expounded first, and then an IoT security threat model is established combined with edge computing (EC) technology. Additionally, the traditional algorithm is improved to be adapted to the application environment of the current United Nations cyberspace governance actions, and is trained and optimized by data sets. Finally, a modification plan is formulated according to the actual test results. In the experiment, EC is used to establish an excellent IoT security threat model with an efficient and accurate algorithm. The result shows that DL technology and EC technology significantly improve the judgment ability of the IoT security threat model and promote the efficiency of network space governance. This model can inspire the application of emerging computer technology to the IoT network and cyberspace governance, guarantee the construction of global information interconnection, and provide a reference for future research.

Keywords: Deep learning; Internet of Things; Security threat model; Cyberspace governance; Edge computing (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13198-021-01533-w

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