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Robustness Evaluation and Detection of Transferable Adversarial Attacks in ML-Based NIDS
Machine learning-based network intrusion detection systems (ML-based NIDS) are vulnerable to adversarial evasion, where malicious samples are perturbed to evade detection and be misclassified as benign. Despite growing research on adversarial attacks and defenses for ML-based NIDS, comparative evalu
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