Metaheuristic-Based Cepstral Sensing Technique for Prolonging Network Lifetime and Mitigating Primary User Emulation Attacks in Cognitive Radio Sensor Network - V. Abilasha; A. Karthikeyan (IEEE Access)
Cognitive Radio Sensor Network (CRSN) impart adaptive access to inactive radio bands, optimizing spectrum utilization. Conversely, by manipulating secondary users, Primary User Emulation Attack (PUEA) create severe threats to security and cause improper spectrum allocation and higher energy usage. To overcome this issue, we present a Cepstral Spectrum Sensing Multi-Hop Whale Optimization Algorithm (CSSMWA) and the proposed cepstral sensing uses a Logarithmic Spectral Derivative algorithm that aids in identifying authentic primary users from mimicked signals due to its feature-revealing properties, elevating the ability to identify the precision of PUEAs, perform better than other methods with low, moderate to high noise levels. Whale optimisation algorithm maintains network performance and energy efficiency while refining sensing parameters and enhancing decision-making. Extensive simulations are used to analyze the proposed algorithm, incorporating important performance parameters including error probability, energy efficiency, residual energy, data gathering efficiency, network longevity, and throughput. According to simulation findings, the proposed algorithm surpasses other methods in terms of privacy, network surveillance capabilities, and spectrum sensing accuracy. For future wireless networks, the work assists in developing CRSN that are resilient, trustworthy, and energy-efficient.