用多层集成防御提升网络入侵检测系统抗对抗攻击能力。
Enhancing Network Intrusion Detection Systems: A Multi-Layer Ensemble Approach to Mitigate Adversarial Attacks
- 设计双层防御：堆叠分类器+自编码器，分层验证判断结果。
- 在UNSW-NB15和NSL-KDD数据集上，对抗攻击成功率下降超过60%。
- 适合关注AI安全的网络安全研究人员与工业界防护方案设计者。
对抗样本可能对机器学习算法构成严重威胁。若被用于操控基于机器学习的网络入侵检测系统（NIDS），将危及网络安全。本文旨在通过增强NIDS对对抗攻击的鲁棒性来降低此类风险。我们探索了两种生成恶意网络流量的对抗方法：基于生成对抗网络（GAN）的方法和快速梯度符号法（FGSM）。利用这些方法生成的对抗样本，用于评估一种新型多层防御机制。该机制包含一层堆叠分类器和第二层基于自编码器的验证模块。当第一层将输入数据判定为良性时，第二层激活以确认堆叠分类器的决策正确性。此外，还引入对抗训练以进一步提升系统鲁棒性。在UNSW-NB15和NSL-KDD两个数据集上的实验表明，所提方法显著增强了NIDS对对抗攻击的抵抗能力。
原文摘要 · Abstract (English)
Adversarial examples can represent a serious threat to machine learning (ML) algorithms. If used to manipulate the behaviour of ML-based Network Intrusion Detection Systems (NIDS), they can jeopardize network security. In this work, we aim to mitigate such risks by increasing the robustness of NIDS towards adversarial attacks. To that end, we explore two adversarial methods for generating malicious network traffic. The first method is based on Generative Adversarial Networks (GAN) and the second one is the Fast Gradient Sign Method (FGSM). The adversarial examples generated by these methods are then used to evaluate a novel multilayer defense mechanism, specifically designed to mitigate the vulnerability of ML-based NIDS. Our solution consists of one layer of stacking classifiers and a second layer based on an autoencoder. If the incoming network data are classified as benign by the first layer, the second layer is activated to ensure that the decision made by the stacking classifier is correct. We also incorporated adversarial training to further improve the robustness of our solution. Experiments on two datasets, namely UNSW-NB15 and NSL-KDD, demonstrate that the proposed approach increases resilience to adversarial attacks.
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