Abstract: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 arXiv-issued DOI via DataCite |
|
| Journal reference: | Concurrency and Computation: Practice and Experience, 2026 |
| Related DOI: | https://doi.org/10.1002/cpe.70637
DOI(s) linking to related resources |
From: Oscar Romero [view email]
[v1]
Thu, 16 Apr 2026 12:53:58 UTC (831 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。