An over-the-air membership inference attack (MIA) is presented to leak private information
from a wireless signal classifier. Machine learning (ML) provides powerful means to classify …

This paper presents how to leak private information from a wireless signal classifier by
launching an over-the-air membership inference attack (MIA). As machine learning (ML) …

Deep learning techniques can classify spectrum phenomena (eg, waveform modulation)
with accuracy levels that were once thought impossible. Although we have recently seen …

Adversarial examples (AdExs) in machine learning for classification of radio frequency (RF)
signals can be created in a targeted manner such that they go beyond general …

Recently, a number of neural network approaches to physical-layer wireless security have
been introduced. In particular, these approaches are able to authenticate the identity of …

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

There has been recent interest in leveraging federated learning (FL) for radio signal
classification tasks. In FL, model parameters are periodically communicated from …

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 …

This paper presents channel-aware adversarial attacks against deep learning-based
wireless signal classifiers. There is a transmitter that transmits signals with different …

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