提升网络入侵检测中对抗鲁棒性的方法：一种分层自适应正则化方法
Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach
AI总结：
本文提出LARAR方法，通过分层漏洞分析和自适应加权提升网络入侵检测系统对抗鲁棒性，实现95.01%的准确率并有效防御FGSM、PGD等攻击。
摘要：
利用神经网络分类器中与梯度相关的漏洞的新一波对抗攻击使网络入侵检测系统更易受此类威胁。尽管最先进的对抗训练方法在生成更鲁棒的分类器方面表现出色，但其可解释性和防御能力受限，因为它们缺乏对不同网络分类器层中对抗攻击传播机制的理解。本文提出了一种名为LARAR（分层对抗鲁棒性自适应正则化）的创新方法，该方法在传统对抗训练方法中整合了额外的分层漏洞分析和自适应加权。此外，我们还利用了'辅助分类器'。LARAR提供了可解释的分层漏洞评分，实现了95.01%的干净准确率，并在UNSW-NB15数据集上提供了更好的对抗攻击防御能力（FGSM、PGD和转移攻击）。通过识别脆弱层，所提出的框架减少了计算复杂性，并实现了对抗样本的早期检测，从而增强了NIDS中对抗防御机制的有效性和可解释性。
英文摘要：
The new wave of adversarial attacks that utilize gradient-related vulnerabilities in neural network-based classifiers makes Network Intrusion Detection Systems more open to such threats. Although state-of-the-art adversarial training methods have shown promising results in producing more robust classifiers, their interpretability and defense ability are limited due to their lack of understanding of how adversarial attacks propagate in different layers of network classifiers. In this paper, we present an insightful approach, called LARAR (Layer-wise Adversarial Robustness using Adaptive Regularization), that incorporates additional layer-wise vulnerability analysis and adaptive weighting in conventional adversarial training methods. Additionally, we utilize 'Auxiliary Classifiers' in our approach. LARAR provides interpretable layer-wise vulnerability scores, achieves a clean accuracy of 95.01%, and provides better robustness against adversarial attacks (FGSM, PGD, and transfer attacks) on the UNSW-NB15 dataset. Through the identification of vulnerable layers, the proposed framework reduces computational complexity and enables the early detection of adversarial samples, thus enhancing the effectiveness and interpretability of adversarial defense mechanisms in NIDS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
