MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security
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. This work examines the role of Intrusion Detection Systems (IDS) as a fundamental component of secure communication networks and discusses the limitations of SDN-based attack detecti...
| Authors | Pablo Benlloch, Oscar Romero, Antonio Leon et al. |
|---|---|
| Primary category | cs.NI |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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