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.

翻译：网络安全是当今数字时代的关键问题，用户要求安全的浏览体验和个人数据保护。本研究探索了机器学习（ML）算法与软件定义网络（SDN）控制器的动态集成，通过自适应决策机制增强网络安全。所提出的方法使系统能够根据观察到的网络流量特征动态选择最合适的ML算法。本研究考察了入侵检测系统（IDS）作为安全通信网络基本组成部分的作用，并讨论了基于SDN的攻击检测机制的局限性。提出的框架采用自适应模型选择，以在不同网络条件下维持可靠的入侵检测。研究强调了分析基于流量类型的指标以定义有效分类规则并提升ML模型性能的重要性。此外，还解决了过拟合与欠拟合的风险，强调了超参数调优在优化模型准确性与泛化能力中的关键作用。本工作的核心贡献在于一种自动化机制，该机制根据实时网络条件自适应选择最合适的ML算法，优先考虑SDN环境下的检测鲁棒性与操作可行性。