FlowUAP: Evading Network Intrusion Detection Systems With Type-Specific Universal Adversarial Perturbations

Summary

FlowUAP is a framework that precomputes type-specific universal adversarial perturbations with a conditional GAN, constrained to a traffic-aware feature space, to evade flow-based network intrusion detection systems. It maps feature-space perturbations to packet-level operations on outbound traffic, avoiding costly per-flow optimization. Experiments on CIC-IDS2018 and ToN-IoT show strong attack effectiveness against diverse NIDS models.

Topics

• Adversarial machine learning

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