Network Traffic Classification Using Ensemble Learning in Software-Defined Networks

Eom, Won-Ju; Song, Yeong-Jun; Park, Chang Hoon; Kim, Jeong-Keun; Kim, Geon-Hwan; Cho, You-Ze

doi:10.1109/icaiic51459.2021.9415187

Cited by 17 publications

(4 citation statements)

References 6 publications

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“…(2) Gradient Boosting Machines (GBM) iteratively build a sequence of DTs, each correcting errors made by previous trees [108]. GBM demonstrates strong performance, but it faces challenges such as overfitting and computational speed [109]. (3) AdaBoost combines weak learners to create a strong learner, giving more weight to misclassified examples [110].…”

Section: Ensemble Learningmentioning

confidence: 99%

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Machine-Learning-Based Traffic Classification in Software-Defined Networks

Serag,

Abdalzaher,

Elsayed

et al. 2024

Electronics

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Many research efforts have gone into upgrading antiquated communication network infrastructures with better ones to support contemporary services and applications. Smart networks can adapt to new technologies and traffic trends on their own. Software-defined networking (SDN) separates the control plane from the data plane and runs programs in one place, changing network management. New technologies like SDN and machine learning (ML) could improve network performance and QoS. This paper presents a comprehensive research study on integrating SDN with ML to improve network performance and quality-of-service (QoS). The study primarily investigates ML classification methods, highlighting their significance in the context of traffic classification (TC). Additionally, traditional methods are discussed to clarify the ML outperformance observed throughout our investigation, underscoring the superiority of ML algorithms in SDN TC. The study describes how labeled traffic data can be used to train ML models for appropriately classifying SDN TC flows. It examines the pros and downsides of dynamic and adaptive TC using ML algorithms. The research also examines how ML may improve SDN security. It explores using ML for anomaly detection, intrusion detection, and attack mitigation in SDN networks, stressing the proactive threat-detection and response benefits. Finally, we discuss the SDN-ML QoS integration problems and research gaps. Furthermore, scalability and performance issues in large-scale SDN implementations are identified as potential issues and areas for additional research.

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Section: Ensemble Learningmentioning

confidence: 99%

“…Ensemble models, which combine the predictions of multiple individual models, have emerged as powerful tools in ML for improving predictive accuracy and robustness [103,109]. Ensemble learning, despite being a potent tool in ML, poses numerous research challenges that demand deeper exploration.…”

Section: Use Of Ensemble Learning Modelsmentioning

confidence: 99%

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Serag,

Abdalzaher,

Elsayed

et al. 2024

Electronics

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“…Eom et al [10] conducted a tra c classi cation study in SDNs using ensemble learning methods. The motivation of this study is to take advantage of the performance of the SDN controller and its global visibility of the network, which facilitates the integration of machine learning on the one hand and on the other hand, uses the advantage offered by ensemble learning algorithms in terms of good performance for network tra c classi cation.…”

Section: Related Workmentioning

confidence: 99%

Traffic Qualification in SD-WMN using Ensemble based Machine Learning Approach

Noutat

Ndie²,

Bessala

2023

Preprint

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The traffic classification problem formulation is NP-hard and has known several resolution approaches where the emerging one is the machine learning approach. However, these approaches have primarily focused on traditional wired and wireless networks and rarely on Software-Defined Wireless Mesh Networks (SD-WMNs). A Software-Defined Network (SDN) makes network monitoring easier by separating the control plane of the network from the data plane. This paper discusses the limits of traffic classification in the network and proposes an approach based on supervised ensemble machine learning adapted to SD-WMN to classifier traffic efficiently in three stages: (a) a traffic-monitoring phase, (b) an IP flow collection phase and, (c) a traffic classification phase by the ensemble supervised machine learning. Ensemble methods are techniques that aim at improving the accuracy of results in models by combining multiple models instead of using a single model. The combined models significantly increase the accuracy of results. We performed experiments on Mininet-wifi emulation platform as data plane with Ryu as SDN controller in control plane. The supervised ensemble learning yields: (a) for the Bagging algorithm with the Random Forest algorithm, an accuracy of 99.90%, with an F1 score of 99.90% and, (b) for Boosting with the XGBoost algorithm, an accuracy of 99.97% with an F1 score of 99.96%. XGBoost appears as the best traffic classification model.

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“…Networks are becoming more complex and dynamic, which has compelled network operators to devise efficient network management methods. Due to the massive amount of generated traffic, traffic classification has become a difficult task in order to distinguish between a variety of applications [2]. The network traffic classification is the foundation of network management, which can manage the corresponding network traffic differently, provide the basis for the subsequent network protocol design, provide the strategies for network attack detection and flow cleaning in network security [3].…”

Section: Introductionmentioning

confidence: 99%

Traffic Classification in Software Defined Networks based on Machine Learning Algorithms

mahgoub,

Ashour,

Yakout

et al. 2024

International Journal of Telecommunications

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A crucial area of research is traffic classification, particularly in light of the advancements in machine learning in software-defined networking. Software-defined networks, which divide the control and data planes, can be automated and controlled by machine learning. Because traditional procedures could not keep up with the expanding use of encryption, the use of techniques for this purpose has increased. In this study, 15 features (the quantity of packets communicated, the average transmission time, and the number of instantly transmitted packets) were used to build traffic flows on the SDN for several protocols, including WWW, DNS, FTP, ICMP, P2P, and VOIP. A real-time dataset was produced by gathering data based on the features that were generated over the SDN controller in the physical network. We use the dataset to test and train a variety of machine learning models, including Random Forest, K Nearest Neighbor, Support Vector Machine, Logistic Regression, Decision Tree, and Naive Bayes. With a 99.8% accuracy rate, Decision Tree emerged as the most successful model for the traffic classification challenge. In order to provide the best classification performance with the lowest cost flow features for traffic classification in SDN, this approach has been identified as machine learning.

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Network Traffic Classification Using Ensemble Learning in Software-Defined Networks

Cited by 17 publications

References 6 publications

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Machine-Learning-Based Traffic Classification in Software-Defined Networks

Traffic Qualification in SD-WMN using Ensemble based Machine Learning Approach

Traffic Classification in Software Defined Networks based on Machine Learning Algorithms

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