Optimized and Secure Routing in Software-Defined Networking Using Reinforcement Learning and Cryptographic Techniques

The increasing complexity of digital communication networks demands efficient, scalable, and secure network management solutions. Software-Defined Networking (SDN) has emerged as a transformative technology, enabling centralized control, real-time traffic management, and adaptive routing. However, existing routing mechanisms often struggle to balance key performance factors such as latency, energy efficiency, and security. To address these challenges, this study presents a comparative analysis of reinforcement learning-based cognitive routing and a security-enhanced, energy-efficient SDN framework. The proposed approach integrates Exponential Spline Regression Reinforcement Learning (ESR-RL) for optimized routing decisions and Genus Weierstrass Curve Cryptography (GWCC) for secure data transmission. The study evaluates various SDN routing techniques based on latency, throughput, energy consumption, and encryption/decryption efficiency. Comparisons with traditional SDN routing, reinforcement learning-based methods, genetic algorithm-based optimizations, and load-balanced approaches demonstrate that the proposed scheme significantly reduces end-to-end delay while maintaining strong security. Additionally, ESR-RL proves effective in minimizing network overhead, while GWCC ensures robust encryption without imposing excessive computational costs. Simulation results indicate that the proposed SDN optimization framework offers superior scalability and adaptability, making it highly suitable for dynamic network environments. The findings highlight that integrating advanced learning-based routing with lightweight cryptographic techniques can significantly enhance SDN performance, making it ideal for future networking applications such as 5G, IoT, and cloud-based infrastructures. This research contributes to the development of more resilient and intelligent SDN frameworks capable of meeting evolving network demands.