AI Based Cognitive and Software Defined Network for Dynamic Management and Security in 6G Networks
Keywords:
6G Networks, Cognitive Networks, Artificial Intelligence, Software-Defined Networking, Dynamic Network Management, Zero-Trust Security, Reinforcement Learning, CNN, Network ScalabilityAbstract
The appearance of the sixth generation (6G) networks introduces new realities of demand towards real-time, scalable and secure communication infrastructures. To respond to such challenges, the overlap of three major technologies is required, namely Cognitive Networks (CN), Artificial Intelligence (AI), and Software-Defined Networking (SDN). Nevertheless, the issue of integration is not simple as the environments of 6G will be heterogeneous, and threats to security will be constantly changing. This paper will suggest a combination of hybrid layered architecture which integrates AI-based analytics, cognitive flexibility and SDN programmability to optimize dynamic network control and advanced security in 6G environments. This architecture styles use of reinforcement learning (RL) and convolutional neural networks (CNN) to enable autonomous decision creation, resource optimization, and detection of threats. The simulation results (obtained with synthetic traffic and attack scenarios) show the improvements compared to the baseline systems are high: 30 percent less latency, 25 percent higher throughput, 20 percent higher energy efficiency, 95 percent DDoS attack detection and 98 percent malware propagation detection accuracies. These results confirm the capabilities of the framework in providing mission critical 6G applications in areas like in healthcare, industries, and smart cities.
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Copyright (c) 2025 Ehsan Qahtan Ahmed, Zainab Haider Ameen, Zahraa A. Jaaz, Maamoun Ahmed
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