提出一套防御机制，提升机器学习网络入侵检测系统抗对抗攻击能力。
A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems
- 融合对抗训练与特征工程，增强模型鲁棒性
- 在NSL-KDD和UNSW-NB15上平均提升35%检测率，降低12.5%误报率
- 适合关注网络安全实战部署的研究者与工程师
随着网络攻击日益复杂，现代网络安全亟需先进的网络入侵检测系统（NIDS）。传统基于签名的NIDS难以应对零日攻击与演进型攻击。为此，基于机器学习（ML）的NIDS应运而生，但易受对抗规避攻击影响，攻击者可微调流量以绕过检测。为解决此问题，我们提出一种新型防御框架，通过同时集成对抗训练、数据集平衡、高级特征工程、集成学习与大规模模型微调，提升ML-based NIDS的鲁棒性。在NSL-KDD与UNSW-NB15数据集上验证，实验结果表明，相比基线模型，平均检测准确率提升35%，误报率降低12.5%，尤其在对抗条件下表现显著。该防御方案极大推进了鲁棒性ML-NIDS在真实网络环境中的实用部署。
原文摘要 · Abstract (English)
As cyberattacks become increasingly sophisticated, advanced Network Intrusion Detection Systems (NIDS) are critical for modern network security. Traditional signature-based NIDS are inadequate against zero-day and evolving attacks. In response, machine learning (ML)-based NIDS have emerged as promising solutions; however, they are vulnerable to adversarial evasion attacks that subtly manipulate network traffic to bypass detection. To address this vulnerability, we propose a novel defensive framework that enhances the robustness of ML-based NIDS by simultaneously integrating adversarial training, dataset balancing techniques, advanced feature engineering, ensemble learning, and extensive model fine-tuning. We validate our framework using the NSL-KDD and UNSW-NB15 datasets. Experimental results show, on average, a 35% increase in detection accuracy and a 12.5% reduction in false positives compared to baseline models, particularly under adversarial conditions. The proposed defense against adversarial attacks significantly advances the practical deployment of robust ML-based NIDS in real-world networks.
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