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

• URL: http://arxiv.org/abs/2604.14957v1
• Date: Thu, 16 Apr 2026 12:53:58 GMT
• Title: MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security
• Abstract summary: This study explores the dynamic integration of Machine Learning algorithms with Software-Defined Networking (SDN) controllers.<n>The proposed approach enables the system to dynamically choose the most suitable ML algorithm based on the characteristics of the observed network traffic.
• Score: 4.262842139213901
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• Toward Autonomous and Efficient Cybersecurity: A Multi-Objective AutoML-based Intrusion Detection System [10.769230030919074]
  Intrusion Detection System (IDS) utilizing Automated Machine Learning (AutoML) and Multi-Objective Optimization (MOO)<n>This work presents the first IDS framework that integrates all four AutoML stages and employs multi-objective optimization to jointly optimize detection effectiveness, efficiency, and confidence for deployment in resource-constrained systems.
  arXiv  Detail & Related papers  (2025-11-11T17:31:42Z)
• Attention Augmented GNN RNN-Attention Models for Advanced Cybersecurity Intrusion Detection [0.4369550829556577]
  We propose a novel hybrid deep learning architecture that synergistically combines Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs) and multi-head attention mechanisms.<n>Our approach effectively captures both spatial dependencies through graph structural relationships and sequential analysis of network events.<n>The integrated attention mechanism provides dual benefits of improved model interpretability and enhanced feature selection, enabling cybersecurity analysts to focus computational resources on high-impact security events.
  arXiv  Detail & Related papers  (2025-10-29T03:47:02Z)
• Intrusion Detection System Using Deep Learning for Network Security [0.6554326244334868]
  This paper proposes an experimental evaluation of IDS models based on deep learning techniques.<n>We focus on the classification of network traffic into malicious and benign categories.<n>Among the tested models, the best achieved an accuracy of 96 percent.
  arXiv  Detail & Related papers  (2025-05-09T06:04:58Z)
• Network Resource Optimization for ML-Based UAV Condition Monitoring with Vibration Analysis [54.550658461477106]
  Condition Monitoring (CM) uses Machine Learning (ML) models to identify abnormal and adverse conditions.<n>This work explores the optimization of network resources for ML-based UAV CM frameworks.<n>By leveraging dimensionality reduction techniques, there is a 99.9% reduction in network resource consumption.
  arXiv  Detail & Related papers  (2025-02-21T14:36:12Z)
• Exploring Feature Importance and Explainability Towards Enhanced ML-Based DoS Detection in AI Systems [3.3150909292716477]
  Denial of Service (DoS) attacks pose a significant threat in the realm of AI systems security. statistical and machine learning (ML)-based DoS classification and detection approaches utilize a broad range of feature selection mechanisms to select a feature subset from networking traffic datasets. In this paper, we investigate the importance of feature selection in improving ML-based detection of DoS attacks.
  arXiv  Detail & Related papers  (2024-11-04T19:51:08Z)
• Extending Network Intrusion Detection with Enhanced Particle Swarm Optimization Techniques [0.0]
  The present research investigates how to improve Network Intrusion Detection Systems (NIDS) by combining Machine Learning (ML) and Deep Learning (DL) techniques. The study uses the CSE-CIC-IDS 2018 and LITNET-2020 datasets to compare ML methods (Decision Trees, Random Forest, XGBoost) and DL models (CNNs, RNNs, DNNs) against key performance metrics. The Decision Tree model performed better across all measures after being fine-tuned with Enhanced Particle Swarm Optimization (EPSO), demonstrating the model's ability to detect network breaches effectively.
  arXiv  Detail & Related papers  (2024-08-14T17:11:36Z)
• Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
  We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
  arXiv  Detail & Related papers  (2021-06-03T16:45:40Z)
• Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks [82.33619654835348]
  Intelligent surface (IRS) has been employed to reshape the wireless channels by controlling individual scattering elements' phase shifts. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity. In this article, we focus on machine learning (ML) approaches for performance in IRS-assisted wireless networks.
  arXiv  Detail & Related papers  (2020-08-29T08:39:43Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Learning High-Level Policies for Model Predictive Control [54.00297896763184]
  Model Predictive Control (MPC) provides robust solutions to robot control tasks. We propose a self-supervised learning algorithm for learning a neural network high-level policy. We show that our approach can handle situations that are difficult for standard MPC.
  arXiv  Detail & Related papers  (2020-07-20T17:12:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.