  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 for intrusion detection. We then attack those
models with a set of chosen evasion attacks. In an attempt to detect those
adversarial attacks, we design and implement multiple transfer learning-based
adversarial detectors, each receiving a subset of the information passed
through the IDS. By combining their respective decisions, we illustrate that
combining multiple detectors can further improve the detectability of
adversarial traffic compared to a single detector in the case of a parallel IDS
design.
