MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security
Pablo Benlloch, Oscar Romero, Antonio Leon, Jaime Lloret
TLDR
MLDAS dynamically selects optimal machine learning algorithms within Software-Defined Networks to enhance security and intrusion detection based on real-time traffic.
Key contributions
- Introduces MLDAS, a system for dynamic ML algorithm selection in Software-Defined Networks.
- Adapts ML models based on real-time network traffic characteristics for robust intrusion detection.
- Addresses limitations of SDN-based attack detection and optimizes ML performance.
- Highlights traffic-type metrics and hyperparameter tuning for effective classification rules.
Why it matters
This paper is important because it provides an adaptive and robust solution for network security in Software-Defined Networks. By dynamically selecting optimal ML algorithms based on real-time traffic, it significantly enhances intrusion detection capabilities, addressing a critical need in today's digital landscape.
Original 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.
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