الجهة البحثية: جامعة العلوم والتكنولوجيا الأردنية
عنوان البحث المنشور:
Resilient intrusion detection system for adversarial attacks on Low-Rate DDoS
سنة النشر: 2025
With adversarial attacks against IDS becoming increasingly sophisticated, new methods are needed to stress-test their vulnerabilities. While GANs have been used for adversarial attacks in other domains, their application to deceive low-rate DDoS-IDS remains largely unexplored. This paper proposes a framework that leverages GANs to generate adversarial examples specifically optimized to evade DDoS-IDS models. We train IDS models on two benchmark datasets (the first dataset: Low-Rate DDoS Dataset 2022, and the second Dataset: “Public dataset for evaluating Port Scan and Slowloris attacks”), then design a GAN-based attack achieving a 99.9% evasion rate. Our experiments reveal a critical architectural flaw in modern DDoS-IDS: even minimal perturbations to low-rate traffic bypass detection. This work establishes a new threat model for DDoS-IDS and underscores the need for adversarial-resistant designs.
رابط البحث المنشور
https://link.springer.com/article/10.1007/s13042-025-02734-6