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Development of Network Security Based on the Neural Network PSD Algorithm.

Jianxun Li1, Song Ji1, Yiran Jiang1

  • 1School of Information Science and Engineering, Baoding University of Technology, Baoding 071000, Hebei, China.

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This summary is machine-generated.

This study introduces a novel approach for network security situational awareness using a genetic algorithm-optimized Elman neural network. The enhanced model significantly improves prediction accuracy for network security assessments.

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Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Network Security

Background:

  • Increasing network security incidents necessitate advanced situational awareness.
  • Traditional methods for network security assessment often suffer from subjectivity and potential inaccuracies.
  • Existing D-S evidence theory models can yield incorrect results due to evidence conflict.

Purpose of the Study:

  • To develop a multilevel network security situation evaluation index system.
  • To enhance the objectivity and accuracy of network security situation assessment using AI.
  • To improve upon traditional D-S evidence theory by incorporating an evidence correction step.

Main Methods:

  • Constructed a multilevel network security situation evaluation index system.
  • Employed a genetic algorithm (GA) optimized Elman neural network for evaluating network security situations.
  • Utilized Elman neural network to generate objective basic probability assignment functions, optimized with the Particle Swarm Optimization (PSO) algorithm.
  • Integrated an evidence correction step into the traditional D-S evidence theory.
  • Applied D-S evidence theory fusion rules for final network security situation assessment.

Main Results:

  • The GA-Elman neural network model achieved a prediction accuracy of 80%.
  • This accuracy is significantly higher than that of traditional D-S models.
  • The proposed model demonstrates improved accuracy in network security assessment and prediction.

Conclusions:

  • The study provides a feasible theoretical framework for accurate network security posture assessment.
  • The findings offer insights for advancing network security development and maintenance.
  • The developed model is significant for ensuring the security of the network environment.