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CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data

Yifan Zeng

TL;DR

CSAGC-IDS tackles the challenge of network intrusion detection under high-dimensional and imbalanced traffic by pairing a data-balancing generator (SC-CGAN) with a cost-sensitive classifier (CSCA-CNN). SC-CGAN uses a self-attention-augmented conditional GAN to synthesize minority-class samples, while CSCA-CNN applies channel attention and a cost-sensitive loss to improve minority-class detection. On NSL-KDD, CSAGC-IDS achieves 91.09% accuracy and 92.04% F1 for binary classification and 84.55% accuracy and 84.52% F1 for five-class classification, with interpretability analyses via LIME and SHAP. This dual-module architecture advances intrusion detection by addressing data imbalance, high dimensionality, and the need for model transparency, offering a practical approach for robust security analytics.

Abstract

As computer networks proliferate, the gravity of network intrusions has escalated, emphasizing the criticality of network intrusion detection systems for safeguarding security. While deep learning models have exhibited promising results in intrusion detection, they face challenges in managing high-dimensional, complex traffic patterns and imbalanced data categories. This paper presents CSAGC-IDS, a network intrusion detection model based on deep learning techniques. CSAGC-IDS integrates SC-CGAN, a self-attention-enhanced convolutional conditional generative adversarial network that generates high-quality data to mitigate class imbalance. Furthermore, CSAGC-IDS integrates CSCA-CNN, a convolutional neural network enhanced through cost sensitive learning and channel attention mechanism, to extract features from complex traffic data for precise detection. Experiments conducted on the NSL-KDD dataset. CSAGC-IDS achieves an accuracy of 84.55% and an F1-score of 84.52% in five-class classification task, and an accuracy of 91.09% and an F1 score of 92.04% in binary classification task.Furthermore, this paper provides an interpretability analysis of the proposed model, using SHAP and LIME to explain the decision-making mechanisms of the model.

CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data

TL;DR

CSAGC-IDS tackles the challenge of network intrusion detection under high-dimensional and imbalanced traffic by pairing a data-balancing generator (SC-CGAN) with a cost-sensitive classifier (CSCA-CNN). SC-CGAN uses a self-attention-augmented conditional GAN to synthesize minority-class samples, while CSCA-CNN applies channel attention and a cost-sensitive loss to improve minority-class detection. On NSL-KDD, CSAGC-IDS achieves 91.09% accuracy and 92.04% F1 for binary classification and 84.55% accuracy and 84.52% F1 for five-class classification, with interpretability analyses via LIME and SHAP. This dual-module architecture advances intrusion detection by addressing data imbalance, high dimensionality, and the need for model transparency, offering a practical approach for robust security analytics.

Abstract

As computer networks proliferate, the gravity of network intrusions has escalated, emphasizing the criticality of network intrusion detection systems for safeguarding security. While deep learning models have exhibited promising results in intrusion detection, they face challenges in managing high-dimensional, complex traffic patterns and imbalanced data categories. This paper presents CSAGC-IDS, a network intrusion detection model based on deep learning techniques. CSAGC-IDS integrates SC-CGAN, a self-attention-enhanced convolutional conditional generative adversarial network that generates high-quality data to mitigate class imbalance. Furthermore, CSAGC-IDS integrates CSCA-CNN, a convolutional neural network enhanced through cost sensitive learning and channel attention mechanism, to extract features from complex traffic data for precise detection. Experiments conducted on the NSL-KDD dataset. CSAGC-IDS achieves an accuracy of 84.55% and an F1-score of 84.52% in five-class classification task, and an accuracy of 91.09% and an F1 score of 92.04% in binary classification task.Furthermore, this paper provides an interpretability analysis of the proposed model, using SHAP and LIME to explain the decision-making mechanisms of the model.

Paper Structure

This paper contains 37 sections, 7 equations, 11 figures, 10 tables.

Table of Contents

  1. Introduce
  2. Background
  3. Research Content and Contributions
  4. SC-CGAN.
  5. CSCA-CNN.
  6. CSAGC-IDS.
  7. Paper Structure
  8. Related Work
  9. Deep Learning Intrusion Detection Methods
  10. Imbalanced Data Processing Methods
  11. Proposed Model for Network Intrusion Detection
  12. CSAGC-IDS Architecture
  13. Imbalanced Data Processing Algorithm SC-CGAN
  14. Generative Adversarial Nets with Fusion of Conditional Information.
  15. Conditional Self Attention Mechanism Generator.
  16. ...and 22 more sections

Figures (11)

  • Figure 1: CSAGC-IDS architecture
  • Figure 2: CSAGC-IDS process
  • Figure 3: SC-CGAN architecture
  • Figure 4: CSAM architecture
  • Figure 5: Residual connection of CSAM
  • ...and 6 more figures