2026 Vol.9 Jun.N03

• Title: A Technique for Multidimensional multi-dimensional feature fusion of Cybersecurity Data Based on Spiking Neural Networks
• Name: Yuting Jiang
• Company: School of Information Engineering,Jiangsu Maritime Institute,Nanjing,211170,China
• Abstract:

   Conventional network security data multi-dimensional feature fusion techniques mainly use CDA homomorphic secure fusion algorithm to set elastic fusion keys, which are easily affected by the end-to-end transmission status of supporting nodes, resulting in poor fusion performance. To address this, a multi-dimensional multi-dimensional feature fusion technology for network security data based on spiking neural network is proposed. The hierarchical description of multi-dimensional network security data features is carried out, covering traffic, attack events, malicious codes, logs, and vulnerabilities. A spiking neural network is employed to generate a multi-dimensional multi-dimensional feature fusion center, which performs authenticity discrimination and multi-attribute fusion processing, thereby achieving comprehensive multi-dimensional multi-dimensional feature fusion. Experimental results show that the proposed technology has good fusion indicators, reliability, and certain application value, contributing to reducing network transmission risks and improving the overall quality of network data.

  
  

• Keyword: Spiking neural network; Network security; Multi-dimensional data features; Multi-dimensional multi-dimensional feature fusion
• DOI: 10.12250/jpciams2026090604
• Citation form: Yuting Jiang.A Technique for Multidimensional multi-dimensional feature fusion of Cybersecurity Data Based on Spiking Neural Networks[J]. Computer Informatization and Mechanical System,2026,Vol.9,pp.

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