Ashraf Matrawy
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The paper empirically evaluates domain-adapted and general-purpose LLMs for structured threat modelling (STRIDE on 5G security), finding that domain adaptation and model size do not guarantee reliable performance, and that decoding strategies significantly impact output validity.
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.
Papers
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 adve…