Categorical Robustness Assessment for Machine Learning based Network Intrusion Detection Systems
Network Intrusion Detection Systems (NIDS) heavily utlize Machine Learning (ML) but ML models can be manipulated via adversarial attacks. These attacks add carefully crafted perturbations to network traffic data that leads to misclassifications. While prior work has demonstrated adversarial vulnerabilities in isolated settings, systematic cross-architecture as well as class and category of attack based comparisons under controlled attack conditions remain limited, leaving practitioners without clear guidance on which models to deploy in adversarial environments. This paper asks a simple question: what type of classifier architectures actually hold up when attackers try to manipulate the s...
| Authors | Mayank Raj, Nathaniel D. Bastian, Lance Fiondella et al. |
|---|---|
| Primary category | cs.CR |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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