Efficient Resource Distribution in Cognitive Radio Network by Fuzzy-Based Cluster Against Attacks

Abstract

In 5G cognitive wireless network, the bandwidth is shared among the primary user (PU) and secondary user (SU) and face security risks from suspicious attacks. Secondary user (SU) networks are enhanced with a security mechanism to support primary users by proposing a mutual protocol for secondary wireless-driven users to collaborate with primary users. During a wireless transmission phase obtained by the secondary user, the cognitive hybrid access point communicates the power signal. Collected energy from primary user is used to communicate with malicious attacks and to acquire transmission capabilities during the wireless information transfer process. In addition, to obtaining optimum performance, a fuzzy-based clustered greedy algorithm is implemented to reduce the potential for interference in PU confidentiality. In the suggested strategy, the effect of injection and the reactive jamming attacks on the wireless transmission phase are analyzed. They can be detected via a convolution neural network to identify and distinguish various attacks. Finally, the simulation results for the proposed protocol and the corresponding resource sharing algorithm not only allow SU to gain transmission opportunities but also boost PU security efficiency in unknown attacks. The results are compared to the existing methods.