  Motivated by cognitive radios, stochastic Multi-Player Multi-Armed Bandits
has been extensively studied in recent years. In this setting, each player
pulls an arm, and receives a reward corresponding to the arm if there is no
collision, namely the arm was selected by one single player. Otherwise, the
player receives no reward if collision occurs. In this paper, we consider the
presence of malicious players (or attackers) who obstruct the cooperative
players (or defenders) from maximizing their rewards, by deliberately colliding
with them. We provide the first decentralized and robust algorithm RESYNC for
defenders whose performance deteriorates gracefully as $\tilde{O}(C)$ as the
number of collisions $C$ from the attackers increases. We show that this
algorithm is order-optimal by proving a lower bound which scales as
$\Omega(C)$. This algorithm is agnostic to the algorithm used by the attackers
and agnostic to the number of collisions $C$ faced from attackers.
