Evading a Machine Learning-based Intrusion Detection System through Adversarial Perturbations
Torgeir Føreid Fladby, Hårek Haugerud, Stefano Nichele, Kyrre Begnum, Anis Yazidi · 2020
Machine-learning based Intrusion Detection and Prevention Systems provide significant value to organizations because they can efficiently detect previously unseen variations of known threats, new threats related to known malware or even zero-day malware, unrelated to any other known threats. However, while such systems prove invaluable to security personnel, researchers have observed that data subject to inspection by behavioral analysis can be perturbed in order to evade detection.