SIOPA-DLMUC: A Self-Improved Orca Predation Algorithm with Deep Learning for Enhancing 5G Enabled Cognitive Radio Network Security

Cognitive Radio Networks (CRN) are pivotal in the 5G era, ensuring efficient spectrum usage for data-intensive applications while their cognitive abilities adapt to the environment, reducing interference and enhancing connectivity. However, amidst the promise of these advancements lies a critical challenge - the detection of malicious users (MUs) within CRNs. A dynamic and cooperative nature of CRNs, where unlicensed secondary consumers share spectrum with licensed primary consumers that opens door to potential vulnerabilities. Detecting and mitigating presence of MUs are vital for maintaining the reliability of network and preventing illegal spectrum access. To address these security challenges and enhance accuracy of decision-making within CRNs, this study introduces self-improved orca predation algorithm with deep learning driven malicious user detection (SIOPA-DLMUC). This novel technique focuses on robust detection and classification of MUs. It operates in two distinct stages: in the first stage, the long short-term memory (LSTM) algorithm is employed for automated MU detection. LSTM, known for its ability to analyze temporal behavior and communication patterns of users within CRNs, plays a critical role in identifying deviations from normal behavior, thus improving the accuracy of MU detection. In the second stage, the SIOPA-based hyperparameter tuning process optimizes LSTM parameters to enhance detection performance further. To validate the effectiveness of the SIOPA-DLMUC algorithm, extensive testing has been performed on a diverse dataset, including four distinct types of attacks: Byzantine attacks, Jamming Attacks, Spectrum Sensing Data Falsification (SSDF) attacks, and Primary User Emulation (PUE) attacks along normal samples. The results consistently demonstrate superior performance of SIOPA-DLMUC algorithm when compared to other deep learning models, showcasing its potential to bolster security and reliability in CRNs operating within the 5G landscape. With its capacity to adapt to a wide range of threats and provide robust security, the SIOPA-DLMUC algorithm represents a promising solution for ensuring the integrity of 5G-assisted Cognitive Radio Networks. The proposed model achieves an impressive accuracy of 93.93% demonstrate an exceptional performance surpassing the traditional models.