Introducing Perturb-ability Score (PS) to Enhance Robustness Against Evasion Adversarial Attacks on ML-NIDS

This paper proposes a novel Perturb-ability Score (PS) that can be used to identify Network Intrusion Detection Systems (NIDS) features that can be easily manipulated by attackers in the problem-space. We demonstrate that using PS to select only non-perturb-able features for ML-based NIDS maintains detection performance while enhancing robustness against adversarial attacks.