RadioShock: Over-the-Air Adversarial Attacks on Wireless Communication

There is an emerging trend of using deep learning (DL) to handle complex tasks in wireless communication systems. However, recent research suggests that DL-enabled communication systems are vulnerable to adversarial attacks. Fortunately, most of these attacks are simulation-based, incapable of handling realistic channels with, e.g., multipath fading, temporal dynamics, and hardware nonlinearity, and hence lack of practicality. To this end, we present RadioShock, an over-the-air adversarial attack against wireless communication systems. Through accurate estimations of channel states for dynamic adaptations, RadioShock is made for real-world communication scenarios.We further introduce a universal compact perturbation generation algorithm, along with a new perturbation constraint strategy, aiming to achieve covert over-the-air adversarial attacks. We implement a RadioShock prototype and conduct extensive experiments using automatic modulation classification systems as a representative application scenario. The results reveal that RadioShock diminishes the accuracy of diverse models utilized in wireless communication systems by up to 52.41%, far more effective than existing simulation-based adversarial attacks in facing real-world applications.