Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
Machine anodized pearl price xbox learning (ML) models are essential to securing communication networks.However, these models are vulnerable to adversarial examples (AEs), in which malicious inputs are modified by adversaries to produce the desired output.Adversarial training is an effective defense method against such attacks but relies on access