Policy Based Synthesis: Data Generation and Augmentation Methods For RF Machine Learning The current dataset generation methods for RF Machine Learning (RFML) tasks consist of either completely synthetically generated data or completely raw digitized data from an RF front end. The synthetic datasets are often unrealistic in terms of waveforms or protocols, and the raw captures are typically unlabeled (or often mislabeled), and can skew machine learning algorithms to focus on nonsalient features. Further, the associated storage and processing requirements are quite large. In this work, a novel dataset generation and augmentation method called policybased synthesis is presented that aims to address the shortcomings of either approach by combining basic protocol knowledge with simulated channel and device impairments to supplement overtheair captures made in a controlled environment. This method permits the learning of salient features and regularizes radio and device anomalies that are not of interest. Practical considerations for collecting and processing data for this hybridized approach are also detailed and examples are provided on a dataset that includes protocols commonly used in the 2.4 GHz ISM band such as Bluetooth and WiFi.

Adversarial Examples in RF Deep Learning: Detection and Physical Robustness 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 corresponding mitigation strategies are scarcely addressed in the published work, with only a handful of recent publications in the RF domain. With minimal waveform perturbation, RF adversarial examples (AdExs) can cause a substantial increase in misclassifications for spectrum sensing survey applications (e.g. ZigBee mistaken for Bluetooth). In this work, two statistical tests for AdEx detection are proposed. One statistical test leverages the peaktoaveragepower ratio (PAPR) of the RF samples. The second test uses the softmax outputs of the machine learning model, which is proportional to the likelihoods the classifier assigns to each of the trained classes. The first test leverages the RF nature of the data while the latter is universally applicable to AdExs regardless of the domain. Both solutions are shown as viable mitigation methods to subvert adversarial attacks against RF waveforms, and their effectiveness is analyzed as function of the propagation channel and type of waveform.