  Deep learning provides powerful means to learn from spectrum data and solve
complex tasks in 5G and beyond such as beam selection for initial access (IA)
in mmWave communications. To establish the IA between the base station (e.g.,
gNodeB) and user equipment (UE) for directional transmissions, a deep neural
network (DNN) can predict the beam that is best slanted to each UE by using the
received signal strengths (RSSs) from a subset of possible narrow beams. While
improving the latency and reliability of beam selection compared to the
conventional IA that sweeps all beams, the DNN itself is susceptible to
adversarial attacks. We present an adversarial attack by generating adversarial
perturbations to manipulate the over-the-air captured RSSs as the input to the
DNN. This attack reduces the IA performance significantly and fools the DNN
into choosing the beams with small RSSs compared to jamming attacks with
Gaussian or uniform noise.
