ABSTRACT
The application of machine learning (ML) in the wireless domain, commonly referred to as radio frequency machine learning (RFML), has grown strongly in recent years to solve various problems in the areas of wireless communications, networking, and signal processing. Machine learning has found applications in wireless security such as user equipment (UE) authentication, intrusion detection, and detection of conventional attacks such as jamming and eavesdropping. On the other hand, wireless systems are vulnerable to machine-learning-based security and privacy attack vectors that have recently been considered in other modalities such as computer vision and natural language processing (NLP). Adversarial machine learning has emerged as a major threat for wireless systems, given its potential to disrupt the machine learning process. As the wireless medium is open and shared, it provides new means for adversaries to manipulate the testing and training processes of machine learning algorithms. To that end, novel techniques are needed to safeguard the wireless attack surface against adversarial machine learning.
