Massive SSDF Attackers Identification in Cognitive Radio Networks by Using Consistent Property

Abstract

To resist probabilistic spectrum sensing data falsification (SSDF) attacks in cognitive radio networks (CRNs), we in this paper propose a simple and effective attacker identification scheme from the perspective of each secondary user's (SU) historical sensing data without any prior knowledge about the strategy of attackers. In the proposed algorithm, the inconsistency property of the historical data within two consecutive sensing slots during a sensing time window is extracted to characterize different attack behaviors. Further, an optimal identification threshold is obtained for each SU and the analytical expressions of identification performance are also derived. Finally, simulation results along with theoretical analysis show the validity of the proposed scheme to defend against massive probabilistic SSDF attacks with low computation cost.