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Network intrusion detection based on a general regression neural network optimized by an improved artificial immune

Jianfa Wu1, Dahao Peng2, Zhuping Li2

  • 1College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China.

Plos One
|March 26, 2015
PubMed
Summary

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This summary is machine-generated.

This study introduces the Artificial Immune Algorithm with Elitist Strategies-General Regression Neural Network (AIAE-GRNN) for robust network intrusion detection. The AIAE-GRNN demonstrates superior accuracy and adaptivity compared to other methods, even after dimensionality reduction.

Area of Science:

  • Cybersecurity
  • Artificial Intelligence
  • Machine Learning

Background:

  • Network intrusion detection is crucial for cybersecurity.
  • General Regression Neural Network (GRNN) offers potential for data classification.
  • Existing optimization algorithms may have limitations in accuracy and adaptivity.

Purpose of the Study:

  • To develop and evaluate an improved GRNN model for network intrusion detection.
  • To enhance the adaptivity and accuracy of GRNN using an artificial immune algorithm with elitist strategies (AIAE).
  • To compare the performance of the proposed AIAE-GRNN against other optimization techniques.

Main Methods:

  • The Artificial Immune Algorithm with Elitist Strategies (AIAE) was developed by integrating elitist archive and crossover with the Artificial Immune Algorithm (AIA).

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  • AIAE was employed to optimize the smooth factors of the General Regression Neural Network (GRNN).
  • Principal Component Analysis (PCA) was utilized for dimensionality reduction to improve processing speed.

    • Main Results:

    • AIAE-GRNN exhibited higher classification accuracy and robustness compared to GRNN optimized with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Fuzzy C-Mean clustering (FCM).
    • FCM-GRNN and GA-GRNN were found to be less accurate and converged slower.
    • PCA-AIAE-GRNN showed improved running speed with minimal accuracy loss and better convergence than PCA-PSO-GRNN.

    Conclusions:

    • The AIAE-GRNN algorithm provides a robust and accurate method for classifying network intrusion data.
    • The integration of AIAE with GRNN significantly enhances model performance.
    • Dimensionality reduction using PCA can improve the efficiency of the AIAE-GRNN model without substantial compromise in accuracy.