Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning

arXiv:2306.05494v2 Announce Type: replace
Abstract: Machine Learning (ML) has become ubiquitous, and its deployment in Network Intrusion Detection Systems (NIDS) is inevitable due to its automated nature and high accuracy compared to traditional models in processing and classifying large volumes of data. However, ML has been found to have several flaws, most importantly, adversarial attacks, which aim to trick ML models into producing faulty predictions. While most adversarial attack research focuses on computer vision datasets, recent studies have explored the …

accuracy adversarial arxiv attacks automated cs.cr cs.lg cs.ni data deployment detection dynamic evasion evasion attacks found high impact intrusion intrusion detection large machine machine learning nature network network intrusion networks nids systems testing