  Reinforcement learning (RL) for network slicing is considered in the 5G radio
access network, where the base station, gNodeB, allocates resource blocks (RBs)
to the requests of user equipments and maximizes 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 5G network
slicing. Subject to an energy budget, the adversary observes the spectrum and
builds its own RL-based surrogate model that selects which RBs to jam with the
objective of maximizing the number of failed network slicing 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 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
mechanisms (protecting the RL algorithm's updates or misleading the adversary's
learning process) are introduced to show that it is viable to defend 5G network
slicing against this attack.
