  In a cognitive radio network, a secondary user learns the spectrum
environment and dynamically accesses the channel where the primary user is
inactive. At the same time, a primary user emulation (PUE) attacker can send
falsified primary user signals and prevent the secondary user from utilizing
the available channel. The best attacking strategies that an attacker can apply
have not been well studied. In this paper, for the first time, we study optimal
PUE attack strategies by formulating an online learning problem where the
attacker needs to dynamically decide the attacking channel in each time slot
based on its attacking experience. The challenge in our problem is that since
the PUE attack happens in the spectrum sensing phase, the attacker cannot
observe the reward on the attacked channel. To address this challenge, we
utilize the attacker's observation capability. We propose online learning-based
attacking strategies based on the attacker's observation capabilities. Through
our analysis, we show that with no observation within the attacking slot, the
attacker loses on the regret order, and with the observation of at least one
channel, there is a significant improvement on the attacking performance.
Observation of multiple channels does not give additional benefit to the
attacker (only a constant scaling) though it gives insight on the number of
observations required to achieve the minimum constant factor. Our proposed
algorithms are optimal in the sense that their regret upper bounds match their
corresponding regret lower-bounds. We show consistency between simulation and
analytical results under various system parameters.
