Omar Abboosh Hussein Gwassi

A Sensitivity-Driven Gradient Framework for Adversarial Sample Generation in Deep Learning-Based Network Intrusion Detection Systems

A sensitivity-driven adversarial generation framework (AGF) that identifies and perturbs the most influential traffic features that affect the classifier’s decision boundary to generate statistically consistent adversarial samples with constrained perturbation magnitude is proposed.