Development of a Severity-Based Attack Mitigation System in Cognitive Radio Networks Using Blockchain and GFHQDC

Cognitive Radio Networks (CRNs) are a form of wireless communication in which sensing devices continuously monitor the network to identify spectrums in use and those that are available. However, existing research has largely overlooked the mitigation of attacks based on their severity levels within CRNs. This oversight leads to issues such as degradation in quality of service, inefficient resource utilization, network instability, and reduced spectrum efficiency. To address these challenges, we propose an effective severity-based attack mitigation system in CRNs, utilizing Logistic Rational Quadratic Fuzzy (LRQF) and Grid Search-based Frequency Hopping with Quantum Debrock Cryptography (GFHQDC). Our approach develops a robust attack severity analysis system for network spectrum threats, mitigating them according to their severity levels. Initially, Primary Users (PUs) and Secondary Users (SUs) are registered with user IDs and passwords, and cryptographic keys are generated and stored in a blockchain. Spectrum sensing is then performed, and PUs are detected. If a PU is present, the spectrum is not allocated; otherwise, spectrum features are extracted and reduced using dimensionality reduction techniques. Outliers are detected, and the attack types and severity levels are identified using LRQF. High-severity attacks are blocked, while low and medium-level attacks are mitigated using GFHQDC. Finally, the optimal SU is selected based on proximity to the PU, and the spectrum is allocated. Our results demonstrate the superiority of the proposed model, achieving a 98.42% security level in attack mitigation, thereby ensuring robust and efficient spectrum sharing within the CRN.