TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems. (arXiv:2210.15700v1 [cs.CR])

Nowadays, intrusion detection systems based on deep learning deliver
state-of-the-art performance. However, recent research has shown that specially
crafted perturbations, called adversarial examples, are capable of
significantly reducing the performance of these intrusion detection systems.
The objective of this paper is to design an efficient transfer learning-based
adversarial detector and then to assess the effectiveness of using multiple
strategically placed adversarial detectors compared to a single adversarial
detector for intrusion detection systems. In our experiments, we implement
existing state-of-the-art models …

adversarial attacks detection evasion intrusion intrusion detection network systems