Hybrid Optimized Secure Cooperative Spectrum Sensing for Cognitive Radio Networks

Abstract

Spectrum utilization is an important factor in Cognitive radio networks, which is accomplished by analyzing the unused spectrum bands of primary users (PU). The secondary users are allowed to access the resources, when the spectral bands are vacant by sensing the spectrum status and thus it reduces the spectrum scarcity among the users. Researchers have paid more attention towards spectrum sensing along with its security factors in cognitive radio networks. In this process, cooperative spectrum sensing is widely adopted in cognitive radio networks due to its robustness. However, the security concerns in cooperative spectrum sensing against attacks must be addressed. The performance of cooperative spectrum sensing will get affected if the fusion center gets wrong information from malicious user. This leads to wrong decision in the fusion center and results into false observations and affects the decision process. In order to overcome these challenges, this research work proposes a hybrid nature inspired and optimized cooperative spectrum sensing against attacks in cognitive radio networks. The proposed model allows the fusion center to remove the uncharacteristic data in the fusion process, which results from the malicious users. The performance analysis of spectrum sensing process under different attacks are analyzed through simulation and later it is compared against conventional methods such as genetic algorithm, particle swarm optimization and differential evolution schemes to validate the improved performance.