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

TL;DR

This paper introduces CSAGC-IDS, a deep learning-based intrusion detection system that effectively handles complex, high-dimensional, and imbalanced network traffic data using generative and attention-enhanced neural networks.

Contribution

It proposes a novel dual-module deep learning model combining SC-CGAN and CSCA-CNN to improve detection accuracy and address class imbalance in network intrusion detection.

Findings

Achieves 84.55% accuracy in five-class classification

Attains 91.09% accuracy in binary classification

Provides interpretability analysis using SHAP and LIME

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.