TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems

TL;DR

This paper proposes a transfer learning-based multi-adversarial detection framework for network intrusion systems, demonstrating that combining multiple detectors enhances the detection of adversarial attacks over single detectors.

Contribution

It introduces a novel multi-adversarial detection approach using transfer learning, improving adversarial attack detection in intrusion detection systems.

Findings

Multiple detectors outperform single detectors in identifying adversarial attacks.

Combining detectors enhances detection accuracy in parallel IDS architectures.

Transfer learning effectively adapts detectors to different attack scenarios.

Abstract

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.