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

Autor:	elShehaby, Mohamed, Matrawy, Ashraf
Rok vydání:	2024
Předmět:	Computer Science - Cryptography and Security Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.07448 Zobrazit plný text záznamu View this record from Arxiv