Hanna Bogucka

This paper investigates a novel physical backdoor attack against Deep Automatic Modulation Classifiers (AMC) in wireless communications, demonstrating that an adversary using Explainable AI (XAI) can efficiently compromise multiple DL models.

This paper proposes a physical backdoor attack against deep learning modulation classifiers, utilizing power amplifier non-linear distortions as physical triggers to achieve high attack success rates.

This paper proposes using adversarial training to proactively harden the k-means clustering classifier, thereby mitigating evasion attacks that threaten resource provisioning stability in fog computing networks.