Soha Rawas, Agariadne Dwinggo Samala
Software-defined networking (SDN) offers centralized control and programmability, but faces challenges in real-time adaptability and autonomous resilience against cyber threats and performance bottlenecks. This paper introduces a novel bio-inspired AI-driven self-healing framework for SDNs that integrates swarm intelligence for anomaly detection, genetic algorithms for network reconfiguration, and reinforcement learning for adaptive traffic optimization. Experimental evaluation using Mininet and the ONOS controller demonstrates that the proposed framework significantly outperforms traditional SDN mechanisms, achieving a 32% enhancement in attack mitigation efficiency, a 47% reduction in network recovery time, and a 29% increase in overall traffic throughput. These results underscore the potential of bio-inspired AI to enable autonomous, resilient, and high-performance networks for next-generation applications in cloud data centers and IoT. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
Faculty of Science, Department of Mathematics and Computer Science, Beirut Arab University, Beirut, Lebanon; Faculty of Engineering, Universitas Negeri Padang, Padang, Indonesia
