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

2026-04-16

AI Summary

This work addresses the challenge of achieving both high accuracy and robustness in intrusion detection under dynamic traffic conditions in Software-Defined Networking (SDN) environments, where conventional security mechanisms often fall short. Fixed machine learning models are particularly susceptible to overfitting or underfitting, leading to performance degradation. To overcome these limitations, the paper proposes an adaptive machine learning framework tailored for SDN, which integrates multiple algorithms within the controller and dynamically selects the optimal model based on real-time traffic characteristics and type-specific metrics. The framework further incorporates automated hyperparameter tuning to enhance model adaptability. Experimental results demonstrate that this approach significantly improves detection performance and generalization capability across diverse network conditions, effectively mitigating model mismatch and thereby strengthening both system security and operational feasibility.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Humans and AI: Human-in-the-loop Machine Learning
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Software-Defined Networking
• Machine Learning
• Intrusion Detection
• Dynamic Algorithm Selection
• Network Security

Innovation

• Dynamic Algorithm Selection
• Software-Defined Networking
• Intrusion Detection System
• Adaptive Machine Learning
• Hyperparameter Tuning