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 …

Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

Deep learning (DL), despite its enormous success in many computer vision and language
processing applications, is exceedingly vulnerable to adversarial attacks. We consider the …

Signal classification is a universal problem in adversarial wireless scenarios, especially
when an eavesdropping radio receiver attempts to glean information about a target …

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

As the applications of deep reinforcement learning (DRL) in wireless communication grow,
sensitivity of DRL based wireless communication strategies against adversarial attacks has …

Wireless communications has greatly benefited in recent years from advances in machine
learning. A new subfield, commonly termed Radio Frequency Machine Learning (RFML) …

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 …

Deep learning-based automatic modulation classification (AMC) models are susceptible to
adversarial attacks. Such attacks inject specifically crafted wireless interference into …

Distributed machine learning (DML) techniques, such as federated learning, partitioned
learning, and distributed reinforcement learning, have been increasingly applied to wireless …