Abstract
Machine learning (ML) models are increasingly deployed in cybersecurity applications such as phishing detection and network intrusion prevention. However, these models remain vulnerable to adversarial perturbations small, deliberate input modifications that can degrade detection accuracy and compromise interpretability. This paper presents an empirical study of adversarial robustness and explainability drift across two cybersecurity domains phishing URL classification and network intrusion detection. We evaluate the impact of L (infinity) bounded Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) perturbations on model accuracy and introduce a quantitative metric, the Robustness Index (RI), defined as the area under the accuracy perturbation curve. Gradient based feature sensitivity and SHAP based attribution drift analyses reveal which input features are most susceptible to adversarial manipulation. Experiments on the Phishing Websites and UNSW NB15 datasets show consistent robustness trends, with adversarial training improving RI by up to 9 percent while maintaining clean-data accuracy. These findings highlight the coupling between robustness and interpretability degradation and underscore the importance of quantitative evaluation in the design of trustworthy, AI-driven cybersecurity systems.
Abstract (translated)
机器学习（ML）模型在网络安全应用中越来越广泛，例如钓鱼检测和网络入侵预防。然而，这些模型仍然容易受到对抗性扰动的影响——即一些小的、故意的输入修改可以降低检测精度并破坏可解释性。本文通过两个网络安全领域：钓鱼网址分类和网络入侵检测，对模型的鲁棒性和可解释性的漂移进行了实证研究。我们评估了L（无穷）界Fast Gradient Sign Method (FGSM) 和 Projected Gradient Descent (PGD) 扰动对模型准确率的影响，并引入了一个量化指标——鲁棒性指数(RI)，定义为精度扰动曲线下的面积。基于梯度的特征敏感性和SHAP归因漂移分析揭示了哪些输入特征最容易受到对抗操作的影响。 在钓鱼网站和UNSW NB15数据集上的实验显示，对抗训练可以将RI提高最多9%，同时保持干净数据的准确率不变。这些发现强调了鲁棒性与可解释性退化的耦合关系，并且突出了在设计可信的人工智能驱动网络安全系统时进行量化评估的重要性。
URL
https://arxiv.org/abs/2602.06395
