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

TL;DR

This paper presents an adaptive framework that dynamically selects the most suitable machine learning algorithms for intrusion detection in SDN networks, improving security under varying traffic conditions.

Contribution

It introduces an automated mechanism for real-time adaptive ML algorithm selection tailored for SDN security, addressing limitations of static approaches.

Findings

Enhanced intrusion detection accuracy through adaptive model selection.

Improved robustness of security systems under diverse network traffic conditions.

Addressed overfitting and hyperparameter tuning for optimal model performance.

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.