Gradient-based adversarial attacks subtly manipulate inputs of Machine Learning (ML) models to induce incorrect predictions. This paper investigates whether careful architectural choices alone can yield an inherently robust Deep Neural Network (DNN)-based Network Intrusion Detection Systems (NIDS), without any additional explicit defenses. Through thousands of experiments, around 2200, varying network depth, feature dimensionality, activation functions, and dropout across FGSM, PGD, and BIM attacks, we show that shallower networks, reduced feature sets, and ReLU activation consistently and jointly reduce adversarial vulnerability. Moreover, a simple model following this recipe outperforms deeper, fully-featured adversarially trained models, while maintaining near-perfect clean-traffic detection and lower training times. Nevertheless, while less is more, the selection of the right less is what truly matters.

翻译：基于梯度的对抗性攻击通过巧妙篡改机器学习模型的输入来诱导错误预测。本文探讨了在无需任何额外显式防御措施的情况下，仅通过精心设计的架构选择能否构建出本质上鲁棒的基于深度神经网络的网络入侵检测系统。通过约2200次涵盖不同网络深度、特征维度、激活函数及Dropout策略（针对FGSM、PGD和BIM攻击）的实验表明：较浅的网络结构、精简的特征集以及ReLU激活函数的联合使用能持续降低对抗攻击脆弱性。此外，遵循此准则的简易模型在保持近乎完美的正常流量检测性能与更低训练时间的同时，其表现优于更深层、全特征且经过对抗训练的模型。然而，尽管"少即是多"，选择正确的"少"才是关键所在。