Defense against primary user emulation attackers based on adaptive Bayesian learning automata in cognitive radio networks

Abstract

Although cognitive radio (CR) provides dynamic spectrum access to combat spectrum scarcity, it imposes some threats to the CR networks. The primary user emulation (PUE) attack is one of these threats in which malicious users try to emulate the primary user signals to prevent the secondary users (SU) from accessing the idle frequency spectrums. In this article, we propose a scheme to defend against the PUE attacker using an adaptive Bayesian learning automaton algorithm named Multichannel Bayesian Learning Automata (MBLA). MBLA uses two different channels simultaneously to have faster and more accurate learning in non-stationary environments and selecting the optimal frequency channel in each time slot. We assume no prior information about the channel statistics like availability probabilities and primary user activities. In this scheme, the SU synchronizes with its receiver using an approach based on the uncoordinated frequency hopping (UFH) and sends its data on different channels, which are obtained by MBLA. We extract the best strategies for the attacker and the SU and then investigate the proposed scheme in terms of the SU throughput in the presence of the PUE attacker. Simulation results are provided to show the convergence speed of the MBLA algorithm and the network performance in terms of the SU throughput and overhead of the control message passing in the CR networks compared to other schemes.