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Network Intrusion Detection Method Combining CNN and BiLSTM in Cloud Computing Environment.

Jing Gao1

  • 1School of Information and Mechatronics Engineering, Zhengzhou Business University, Henan, Gongyi 451200, China.

Computational Intelligence and Neuroscience
|May 9, 2022
PubMed
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This study introduces a novel network intrusion detection method using Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The combined approach enhances detection accuracy and reduces false positive rates for improved network security.

Area of Science:

  • Cybersecurity
  • Machine Learning
  • Network Security

Background:

  • Network intrusion detection systems (NIDS) are crucial for cybersecurity.
  • Traditional methods often struggle with complex, high-dimensional network traffic data.
  • The need for advanced feature extraction and classification techniques is evident.

Purpose of the Study:

  • To propose a novel network intrusion detection method combining CNN and BiLSTM.
  • To enhance the accuracy and efficiency of detecting network intrusions.
  • To leverage deep learning for improved feature representation of network data.

Main Methods:

  • Preprocessing the KDD CUP 99 dataset using data extraction, cleaning, and mapping to create an image dataset.
  • Utilizing CNN for parallel local feature extraction and BiLSTM for long-distance dependent feature extraction.

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  • Integrating an attention mechanism to improve classification accuracy.
  • Combining C5.0 decision tree with the CNN-BiLSTM model for direct learning of high-dimensional data features, bypassing traditional feature selection.

    • Main Results:

    • The proposed method achieved an average accuracy of 95.50%.
    • The false-positive rate was reduced to 4.24%.
    • The false negative rate was reduced to 6.66%.
    • Outperformed existing methods like AE-AlexNet and SGM-CNN in network intrusion detection.

    Conclusions:

    • The CNN-BiLSTM deep learning model offers a significant improvement in network intrusion detection performance.
    • The integrated approach effectively handles complex network traffic data.
    • This method enhances the overall capability of network intrusion detection systems.