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

Author

Listed:
  • Zhi Li

    (NingboTech University)

  • Yuemeng Ge

    (City University of Macau)

  • Jieying Guo

    (NingboTech University)

  • Mengyao Chen

    (NingboTech University)

  • Junwei Wang

    (NingboTech University)

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.

Suggested Citation

  • Zhi Li & Yuemeng Ge & Jieying Guo & Mengyao Chen & Junwei Wang, 2022. "Security threat model under internet of things using deep learning and edge analysis of cyberspace governance," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1164-1176, December.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01533-w
    DOI: 10.1007/s13198-021-01533-w
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    References listed on IDEAS

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    1. Yang, Huixiao & Chen, Wenbo, 2020. "Game modes and investment cost locations in radio-frequency identification (RFID) adoption," European Journal of Operational Research, Elsevier, vol. 286(3), pages 883-896.
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    Cited by:

    1. Ihab K. A. Hamdan & Wulamu Aziguli & Dezheng Zhang & Eli Sumarliah, 2023. "Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 549-568, March.

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