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.

翻译：对抗攻击对基于机器学习的入侵检测系统构成日益严重的威胁：网络流特征中难以察觉的扰动可系统性误导分类器，将恶意流量误判为良性。IDS-Anta框架通过Z-score归一化、奇异值分解与基于汤普森采样的多臂赌博机分类器选择部分缓解了该问题，但其分类器集合缺乏足够的结构多样性以形成稳健的对抗防御。本文提出IDS-Anta++方法，将XGBoost与LightGBM梯度提升模型纳入集成框架，并通过三层黑盒防御机制包裹扩展后的分类器池：孤立森林异常筛查、中位数特征平滑及六路多数投票。在CIC-IDS-2017、CEC-CIC-IDS-2018和CIC-DDoS-2019数据集上，分别针对快速梯度符号法与零阶优化攻击进行实验，结果表明：该方法在干净数据上检测准确率超过99%，且在对抗条件下相较于基线IDS-Anta配置表现出可量化的鲁棒性提升。