MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security

Computer Science > Networking and Internet Architecture [Submitted on 16 Apr 2026] MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security Pablo Benlloch, Oscar Romero, Antonio Leon, Jaime Lloret 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 detection mechanisms. The proposed framework uses adaptive model selection to maintain reliable intrusion detection under varying network conditions. The study highlights the importance of analyzing traffic-type-based metrics to define effective classification rules and enhance the performance of ML models. Additionally, it addresses the risks of overfitting and underfitting, underscoring the critical role of hyperparameter tuning in optimizing model accuracy and generalization. The central contribution of this work is an automated mechanism that adaptively selects the most suitable ML algorithm according to real-time network conditions, prioritizing detection robustness and operational feasibility within SDN environments. Comments: 22 pages, 15 figures, 12 tables Subjects: Networking and Internet Architecture (cs.NI); Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2604.14957 [cs.NI]   (or arXiv:2604.14957v1 [cs.NI] for this version)   https://doi.org/10.48550/arXiv.2604.14957 Focus to learn more Journal reference: Concurrency and Computation: Practice and Experience, 2026 Related DOI: https://doi.org/10.1002/cpe.70637 Focus to learn more Submission history From: Oscar Romero [view email] [v1] Thu, 16 Apr 2026 12:53:58 UTC (831 KB) Access Paper: view license Current browse context: cs.NI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CR cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)