AI RESEARCH
MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security
arXiv CS.LG
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ArXi:2604.14957v1 Announce Type: cross Network security is a critical concern in the digital landscape of today, with users demanding secure browsing experiences and protection of their personal data. This study explores the dynamic integration of Machine Learning (ML) algorithms with Software-Defined Networking (SDN) controllers to enhance network security through adaptive decision mechanisms. The proposed approach enables the system to dynamically choose the most suitable ML algorithm based on the characteristics of the observed network traffic.