Mohamed elShehaby
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126
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ML×1Crypto×1
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2026
A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
The paper demonstrates that simpler, shallower Deep Neural Network architectures with reduced features and ReLU activations can inherently improve the robustness of ML-NIDS against gradient-based adversarial attacks, often outperforming complex, adversarially trained models.
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