Breaking and Healing: GAN-Based Adversarial Attacks and Post-Adversarial Recovery for 5G IDSs

Generative adversarial networks (GANs) have advanced rapidly in data augmentation and generation, and researchers have been exploring their applications in other areas, including adversarial attack generation. GANs have significantly improved the field of adversarial attacks, especially against intrusion detection systems (IDSs). These highly sophisticated GAN-based attacks pose a significant threat to IDS security. Addressing the challenges posed by GAN-based adversarial attacks is crucial for assessing their impact and developing robust defense mechanisms. In this paper, we propose new adversarial attack generation techniques derived from advanced GAN architectures. The effectiveness of these attacks, which are based on sophisticated types of GANs, is evaluated against three multi-class IDSs designed for 5G networks. Comprehensive experiments are conducted to evaluate the effectiveness of each GAN in exploiting IDS vulnerabilities and examine the transferability of adversarial attacks across different IDSs, considering the quality of the adversarial samples generated. Exploring these sophisticated GANs for adversarial attack generation enables us to develop a new post-adversarial recovery mechanism based on reconstructing the adversarial samples. Moreover, to thoroughly assess the capabilities of the proposed techniques, new evaluation metrics are introduced to facilitate a comprehensive analysis of the system’s vulnerabilities. Our results show that the proposed GAN-based adversarial attacks can significantly impact IDSs by achieving a high attack success rate (ASR) and drastically reducing accuracy, recall, precision, and F1 score. However, the proposed post-adversarial recovery process effectively restores the IDSs’ performance while significantly reducing the ASR.