  Due to delay and energy constraints, a cognitive radio may not be able to
perform spectrum sensing in all available channels. Therefore, a sensing policy
is needed to decide which channels to sense. The channel selection problem is
the problem of designing such a sensing policy to maximize throughput while
avoiding interference to primary users. The channel selection problem can be
formulated as a reinforcement learning problem. Channel selection schemes that
employ reinforcement machine learning algorithms are vulnerable to belief
manipulation attacks that contaminate the knowledge base of the learning
algorithms. In this paper, we analyze the security of channel selection
algorithms that are based on reinforcement learning and propose mitigation
techniques that make these algorithms more robust against belief manipulation
attacks.
