Evaluating the Robustness and Transferable Adversarial Example Resistance of Multimodal Learning-based Intrusion Detection Systems against Evasion Attacks
Aug 2026· Journal on spesial topics in mobile networks and applications· Vol 31, pp. 287 - 305· 0 citations· 40 references
TL;DR
This study evaluates the robustness of multimodal learning-based IDS against transferable adversarial examples (AEs) generated by Generative Adversarial Networks (GANs) and integrates adversarial training techniques to enhance the IDS’s capability to identify attack patterns with small perturbations.
This study investigates the transferability of adversarial attacks across XGBoost and LightGBM models in IDS and uses Explainable AI (XAI) techniques to identify features that influence model decisions and shows that feature-importance patterns can be used to examine model vulnerability under adversarial perturbations.
Abdlelah Abdlatef, Marwa D. Abdulkareem, Duygu Çakır· International Journal of Inf...· 1 citation
The proposed adversarially trained ensemble achieves the strongest overall trade-off between classification performance on the robustness evaluation set and evasion resistance, reducing the average ASR from 0.41 to 0.03 and achieving an RI of 0.98.
The scalable and efficient solution against adversarial DDoS attacks, introduced in this work, paves the way towards more resilient and adaptive network defense systems that combine generative adversarial augmentation with recent advances in learning models.
Makram Chehayeb, W. Fahs, A. Rizk et al.· 0 citations
A sensitivity-driven adversarial generation framework (AGF) that identifies and perturbs the most influential traffic features that affect the classifier’s decision boundary to generate statistically consistent adversarial samples with constrained perturbation magnitude is proposed.
Omar Abboosh Hussein Gwassi, O. N. Uçan· IEEE Access· 0 citations
This work proposes PR-based UAP, which represents the first integration of an explicit PR-driven objective into generating UAPs against DRL-based IDS and introduces PX-UAP, which leverages explainable artificial intelligence (XAI) to guide perturbation shaping under realistic domain constraints, and provides a rigorous...
Hong-Sen Zhang, Lu Zhang, Ming-Jing Xu et al.· 0 citations
The rapid expansion of Internet of Things (IoT) environments has introduced significant security challenges, particularly due to limited labeled data and increasing vulnerability to adversarial attacks. Traditional intrusion detection systems (IDS) struggle to generalize under such constraints, while existing few-shot...
Fatima A. Asiri, Nazik Alturki, Noha Alnazzawi et al.· Cluster Computing· 0 citations
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