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cs.LG2026
A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
Mohamed elShehaby, Ashraf Matrawy
Gradient-based adversarial attacks subtly manipulate inputs of Machine Learning (ML) models to induce incorrect predictions. This paper investigates whether careful architectural c…
cs.LG2024
Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance ML Robustness
Mohamed elShehaby, Aditya Kotha, Ashraf Matrawy
Adversarial training enhances the robustness of Machine Learning (ML) models against adversarial attacks. However, obtaining labeled training and adversarial training data in netwo…