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Network Intrusion Detection Method Based on FCWGAN and BiLSTM.

Zexuan Ma1, Jin Li1, Yafei Song1

  • 1College of Air and Missile Defense, Air Force Engineering University, Xi'an 710051, China.

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Summary
This summary is machine-generated.

This study introduces a novel method using feature selection and generative adversarial networks to improve network intrusion detection models. The approach enhances accuracy by addressing imbalanced datasets, leading to more effective threat identification.

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Area of Science:

  • Cybersecurity
  • Machine Learning
  • Data Science

Background:

  • Imbalanced datasets in intrusion detection systems (IDS) lead to biased models, high false positives, and false negatives.
  • Existing methods struggle to effectively handle class imbalance, compromising network security analysis.
  • Accurate network intrusion detection is crucial for protecting sensitive data and infrastructure.

Purpose of the Study:

  • To propose a novel method for enhancing network intrusion detection models by addressing class imbalance.
  • To improve the accuracy and effectiveness of intrusion detection systems.
  • To reduce false-positive and false-negative rates in network traffic analysis.

Main Methods:

  • A feature selection method using XGBoost and Spearman's correlation coefficient to identify and filter relevant features.
  • A conditional Wasserstein generative adversarial network (CWGAN) to generate synthetic samples for dataset augmentation.
  • A bidirectional long short-term memory (BiLSTM) network for model training and classification.

Main Results:

  • The proposed feature selection-conditional Wasserstein generative adversarial network (FCWGAN) and BiLSTM model achieved high accuracy rates of 99.57% on the NSL-KDD dataset and 85.59% on the UNSW-NB15 dataset.
  • The model demonstrated superior performance compared to a similar conditional WGAN and deep neural network (CWGAN-DNN) model, with accuracy improvements of 1.44% and 2.98%, respectively.
  • The method effectively mitigates the impact of class imbalance, enhancing the detection capabilities of intrusion detection models.

Conclusions:

  • The proposed FCWGAN-BiLSTM method offers a robust solution for improving network intrusion detection accuracy on imbalanced datasets.
  • Feature selection and generative adversarial networks are effective in enhancing the performance of deep learning models for cybersecurity.
  • This approach provides a significant advancement in building more reliable and effective network intrusion detection systems.