  In this work I study the problem of adversarial perturbations to rewards, in
a Multi-armed bandit (MAB) setting. Specifically, I focus on an adversarial
attack to a UCB type best-arm identification policy applied to a stochastic
MAB. The UCB attack presented in [1] results in pulling a target arm K very
often. I used the attack model of [1] to derive the sample complexity required
for selecting target arm K as the best arm. I have proved that the stopping
condition of UCB based best-arm identification algorithm given in [2], can be
achieved by the target arm K in T rounds, where T depends only on the total
number of arms and $\sigma$ parameter of $\sigma^2-$ sub-Gaussian random
rewards of the arms.
