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Communication network increases the productivity of every field. So many of attacks were developed by intruders to lay down a running system for unethical means. Some of research area work on feature optimization and transformation methods to increases the prediction accuracy like image processing. This paper has proposed a model that identifies the set of effecting features by use of moth flame optimization model. Selected features were further transformed to get the feature set having high variance, for this principal component analysis (PCA) was implemented. Optimized feature set were used for the training of learning model. Experiment was done on real dataset of computer network having different set of attacks. Result shows that the proposed model has improved the work efficiency.

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Neural Network-Based Security System by PCA and Moth Flame Optimized Features Learning Model

  • 会议论文

  • 发表日期：

    2025年

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  • Abhilasha Sinha,
  • Rakesh Kumar Tiwari,
  • Onkar Nath Thakur

摘要

Communication network increases the productivity of every field. So many of attacks were developed by intruders to lay down a running system for unethical means. Some of research area work on feature optimization and transformation methods to increases the prediction accuracy like image processing. This paper has proposed a model that identifies the set of effecting features by use of moth flame optimization model. Selected features were further transformed to get the feature set having high variance, for this principal component analysis (PCA) was implemented. Optimized feature set were used for the training of learning model. Experiment was done on real dataset of computer network having different set of attacks. Result shows that the proposed model has improved the work efficiency.