AI RESEARCH
A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
arXiv CS.LG
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ArXi:2605.18666v1 Announce Type: new 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.