  Machine learning provides automated means to capture complex dynamics of
wireless spectrum and support better understanding of spectrum resources and
their efficient utilization. As communication systems become smarter with
cognitive radio capabilities empowered by machine learning to perform critical
tasks such as spectrum awareness and spectrum sharing, they also become
susceptible to new vulnerabilities due to the attacks that target the machine
learning applications. This paper identifies the emerging attack surface of
adversarial machine learning and corresponding attacks launched against
wireless communications in the context of 5G systems. The focus is on attacks
against (i) spectrum sharing of 5G communications with incumbent users such as
in the Citizens Broadband Radio Service (CBRS) band and (ii) physical layer
authentication of 5G User Equipment (UE) to support network slicing. For the
first attack, the adversary transmits during data transmission or spectrum
sensing periods to manipulate the signal-level inputs to the deep learning
classifier that is deployed at the Environmental Sensing Capability (ESC) to
support the 5G system. For the second attack, the adversary spoofs wireless
signals with the generative adversarial network (GAN) to infiltrate the
physical layer authentication mechanism based on a deep learning classifier
that is deployed at the 5G base station. Results indicate major vulnerabilities
of 5G systems to adversarial machine learning. To sustain the 5G system
operations in the presence of adversaries, a defense mechanism is presented to
increase the uncertainty of the adversary in training the surrogate model used
for launching its subsequent attacks.
