Dogfight in Spectrum: Combating Primary User Emulation Attacks in Cognitive Radio Systems—Part II: Unknown Channel Statistics

Abstract

The defense against the Primary User Emulation Attack (PUE) is studied in the scenario of unknown channel statistics (coined blind dogfight in spectrum). The algorithm of the adversarial bandit problem is adapted to the context of blind dogfight. Both cases of complete and partial information about the rewards of different channels are analyzed. Performance bounds are obtained subject to arbitrary channel statistics and attack policy. Several attack strategies, namely uniformly random, selectively random and maximal interception attacks, are discussed. The validity of the defense strategy is then demonstrated by numerical simulation results.