Wireless systems are vulnerable to various attacks such as jamming and eavesdropping
due to the shared and broadcast nature of wireless medium. To support both attack and …

We consider a communication scenario, in which an intruder tries to determine the
modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the …

Adversarial attack strategies have been widely studied in machine learning applications,
and now are increasingly attracting interest in wireless communications as the application of …

While research on adversarial examples in machine learning for images has been prolific,
similar attacks on deep learning (DL) for radio frequency (RF) signals and their mitigation …

Internet of Things (IoT) based on cognitive radio (CR) exhibits strong dynamic sensing and
intelligent decision-making capabilities by effectively utilizing spectrum resources. The …

While research on adversarial examples (AdExs) in machine learning for images has been
prolific, similar attacks on deep learning (DL) for radio frequency (RF) signals and …

Adversarial attacks and defenses in machine learning and deep neural network (DNN) have
been gaining significant attention due to the rapidly growing applications of deep learning in …

Modulation classification is an important component of cognitive self-driving networks.
Recently many ML-based modulation classification methods have been proposed. We have …

We show that end-to-end learning of communication systems through deep neural network
autoencoders can be extremely vulnerable to physical adversarial attacks. Specifically, we …

This paper presents an adversarial machine learning approach to launch jamming attacks
on wireless communications and introduces a defense strategy. In a cognitive radio network …