Experiments with around 2200 variations show that shallower networks with reduced features and ReLU activation reduce adversarial vulnerability in ML-NIDS and outperform deeper adversarially trained models while keeping high clean-data performance.
Hyper-parameter tuning for adversarially robust models,
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A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
Experiments with around 2200 variations show that shallower networks with reduced features and ReLU activation reduce adversarial vulnerability in ML-NIDS and outperform deeper adversarially trained models while keeping high clean-data performance.