Securing It Networking Environment in Cran Using Dehaene–changeux Model Driven Moth-flame Optimization

In the dynamic landscape of telecommunications, the evolution of Communication Radio Access Networks (CRAN) has introduced unprecedented challenges to the security of IT networking environments. As the demand for high-speed connectivity and seamless data transmission grows, safeguarding CRAN becomes paramount. With the proliferation of cyber-attacks and the complexity of CRAN architecture, conventional security measures prove insufficient, necessitating an innovative and adaptive approach. Existing methodologies lack the adaptability required to combat emerging threats effectively. This research bridges this gap by proposing the integration of the Dehaene–Changeux Model, renowned for its applicability in cognitive neuroscience, with Moth-Flame Optimization, a nature-inspired algorithm known for its efficiency in solving complex optimization problems. This research addresses the pressing need for a robust security framework using the Dehaene–Changeux Model Driven Moth-Flame Optimization approach. It elucidates the utilization of the Dehaene–Changeux Model to mimic cognitive responses, coupled with Moth-Flame Optimization for real-time adaptability. These models form a dynamic defense mechanism against evolving security threats in the CRAN environment. Results obtained from simulation and testing validate the efficacy of the proposed security model. The adaptive nature of the Dehaene–Changeux Model, combined with the optimization capabilities of Moth-Flame Optimization, showcases a significant enhancement in CRAN security. The research contributes a pioneering solution to fortify IT networking environments in CRAN, ensuring resilience against current and future cyber threats.