Abstract
The rapid evolution of communication networks, particularly Software-Defined Networking (SDN) and next-generation communication infrastructures, has introduced new challenges in securing these dynamic and complex environments. Among the most persistent threats are Distributed Denial of Service (DDoS) attacks, which can disrupt critical services and inflict severe economic and operational damages. To combat these threats, novel and adaptive DDoS detection mechanisms are crucial. This paper proposes a Bayesian Regularization (BR) optimization-based approach for DDoS detection in SDN and next-generation communication networks. Bayesian Regularization is a statistical technique that combines the strength of Bayesian analysis with optimization methodologies, enabling the model to adapt to changing network conditions and attack strategies. This approach leverages the inherent advantages of SDN, such as centralized control and real-time network monitoring, to enhance the accuracy and timeliness of DDoS detection.
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