Lu-Yao Liu

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

Adversarial attacks pose a significant threat to machine learning-based network intrusion detection systems (NIDS) by injecting crafted perturbations into malicious traffic to evade detection. However, many existing attacks rely on per-flow optimization after observing complete flows, which incurs high computational co...