提升入侵检测系统抗攻击能力，用多模型融合与三层防护实现高鲁棒性
SHIELD-IDS: Structurally Heterogeneous Ensemble with Integrated Layered Defense for Intrusion Detection Systems
- 引入XGBoost、LightGBM等多元模型构建异构集成，增强分类器多样性
- 在三种数据集上对抗攻击下仍保持99%以上检测准确率，显著优于基线
- 设计隔离森林、特征平滑、多路投票三层黑盒防御，适合安全敏感场景
对抗攻击对基于机器学习的入侵检测系统构成严重威胁，微小扰动即可误导分类器将恶意流量误判为正常。现有IDS-Anta框架通过Z-score归一化、奇异值分解和基于汤普森采样的多臂赌博机选择机制部分缓解此问题，但其分类器池结构多样性不足，难以抵御复杂攻击。本文提出IDS-Anta++，将XGBoost与LightGBM梯度提升模型纳入集成，并在扩展后的分类器集合外嵌入三层黑盒防御：孤立森林异常筛查、中值特征平滑及六路多数表决。在CIC-IDS-2017、CEC-CIC-IDS-2018与CIC-DDoS-2019数据集上，针对FGSM与零阶优化（ZOO）攻击的实验表明，干净数据下检测准确率超过99%，且在对抗条件下相较基线配置具有明显鲁棒性提升。
原文摘要 · Abstract (English)
Adversarial attacks pose a serious and growing threat to Machine Learning (ML)-based Intrusion Detection Systems (IDS), where imperceptible perturbations to network flow features can systematically mislead classifiers into accepting malicious traffic as benign. The IDS-Anta framework partially addresses this through Z-score normalization, Singular Value Decomposition (SVD), and Multi-Armed Bandit (MAB) classifier selection with Thompson Sampling, yet its classifier pool lacks sufficient structural diversity for robust adversarial resistance. This work introduces IDS-Anta++, which incorporates XGBoost and LightGBM gradient boosting models into the ensemble and wraps the extended pool in a three-layer black-box defense: Isolation Forest anomaly screening, median feature smoothing, and six-way majority voting. Experiments conducted on CIC-IDS-2017, CEC-CIC-IDS-2018, and CIC-DDoS-2019 under both Fast Gradient Sign Method (FGSM) and Zeroth Order Optimization (ZOO) attacks confirm detection accuracy above 99% on clean data, with measurable robustness gains under adversarial conditions relative to the baseline IDS-Anta configuration.
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