针对复杂网络流量与数据不平衡问题，提出双模块深度学习检测模型。
CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data
- 用自注意力增强的生成对抗网络合成数据缓解类别不平衡
- 融合代价敏感与通道注意力的卷积网络提升复杂流量识别精度
- 在NSL-KDD数据集上实现91%以上准确率，适合安全研究者参考
随着计算机网络的普及，网络入侵威胁日益严重，网络入侵检测系统对安全保障至关重要。尽管深度学习在入侵检测中表现良好，但在处理高维复杂流量模式和类别不平衡数据方面仍面临挑战。本文提出CSAGC-IDS，一种基于深度学习的网络入侵检测模型。该模型集成SC-CGAN——一种自注意力增强的卷积条件生成对抗网络，用于生成高质量数据以缓解类别不平衡；同时集成CSCA-CNN——一种结合代价敏感学习与通道注意力机制的卷积神经网络，用于从复杂流量数据中提取特征以实现精准检测。在NSL-KDD数据集上的实验表明，五分类任务中准确率为84.55%，F1得分为84.52%；二分类任务中准确率达91.09%，F1得分为92.04%。此外，本文还利用SHAP和LIME对模型决策过程进行了可解释性分析。
原文摘要 · Abstract (English)
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.
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