  In this paper, reinforcement learning (RL) for network slicing is considered
in NextG radio access networks, where the base station (gNodeB) allocates
resource blocks (RBs) to the requests of user equipments and aims to maximize
the total reward of accepted requests over time. Based on adversarial machine
learning, a novel over-the-air attack is introduced to manipulate the RL
algorithm and disrupt NextG network slicing. The adversary observes the
spectrum and builds its own RL based surrogate model that selects which RBs to
jam subject to an energy budget with the objective of maximizing the number of
failed requests due to jammed RBs. By jamming the RBs, the adversary reduces
the RL algorithm's reward. As this reward is used as the input to update the RL
algorithm, the performance does not recover even after the adversary stops
jamming. This attack is evaluated in terms of both the recovery time and the
(maximum and total) reward loss, and it is shown to be much more effective than
benchmark (random and myopic) jamming attacks. Different reactive and proactive
defense schemes (protecting the RL algorithm's updates or misleading the
adversary's learning process) are introduced to show that it is viable to
defend NextG network slicing against this attack.
