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Related Experiment Video

Updated: Sep 25, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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An Adaptive Information Security System for 5G-Enabled Smart Grid Based on Artificial Neural Network and Case-Based

Chengzhi Jiang1,2, Hao Xu1,2, Chuanfeng Huang1

  • 1School of Economics and Management, Nanjing Institute of Technology, Nanjing, China.

Frontiers in Computational Neuroscience
|May 2, 2022
PubMed
Summary

This study introduces a neuroscience-inspired security system for 5G Internet of Things (IoT) in smart grids. The system enhances data transmission efficiency and effectively defends against various cyberattacks, reducing security risks.

Keywords:
artificial neural networkcase-based learninginformation securitysmart gridzero trust

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Cybersecurity

Background:

  • The integration of 5G Internet of Things (IoT) in power systems enhances smart grid efficiency through increased two-way interactions.
  • However, this deployment introduces new vulnerabilities and attack surfaces that traditional security systems struggle to address promptly.
  • Neuroscience-inspired models offer promising solutions for complex security and optimization challenges within smart grids.

Purpose of the Study:

  • To develop an advanced information security system for 5G IoT in smart grids.
  • To improve security mechanisms while optimizing data transmission efficiency using neuroscience-inspired algorithms.
  • To address the challenge of enhancing smart grid security in the context of 5G IoT deployment.

Main Methods:

  • An information security system integrating an artificial neural network (ANN) and an improved multiple protection model was proposed.
  • The ANN algorithm trained on link state sample space to determine optimal transmission paths in 5G power communication networks.
  • A multi-layered protection strategy was implemented, including a zero-trust platform with case-based learning, network logical isolation, and forward/backward isolation facilities.

Main Results:

  • The proposed system demonstrated efficiency and effectiveness in experimental evaluations.
  • The system successfully resisted various cyberattacks, including malicious terminal access, hijacking, data tampering, eavesdropping, protocol fuzzing, and denial-of-service attacks.
  • Security risks associated with 5G IoT in smart grids were significantly reduced.

Conclusions:

  • The developed system provides strengthened security for 5G IoT in smart grids.
  • The proposed algorithms are effective in defending against a wide range of cyber threats.
  • The system's compatibility with existing smart grid infrastructure in China makes it a valuable reference for future 5G IoT implementations.