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Detection and classification of conflict flows in SDN using machine learning algorithms

Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN...

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Main Authors:	Mutaz Hamed Hussien Khairi, Sharifah Hafizah Syed Ariffin, Nurul Mu'azzah Abdul Latiff, Kamaludin Mohamad Yusof, Mohamed Khalafalla Hassan, Fahad Taha Al-Dhief, Mosab Hamda, Suleman Khan, Muzaffar Hamzah
Format:	Article
Language:	English English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	QA Mathematics TJ Mechanical engineering and machinery
Online Access:	https://eprints.ums.edu.my/id/eprint/29969/1/Detection%20and%20classification%20of%20conflict%20flows%20in%20SDN%20using%20machine%20learning%20algorithms-Abstract.pdf https://eprints.ums.edu.my/id/eprint/29969/2/Detection%20and%20classification%20of%20conflict%20flows%20in%20SDN%20using%20machine%20learning%20algorithms.pdf https://eprints.ums.edu.my/id/eprint/29969/ https://ieeexplore-ieee-org.ezproxy.ums.edu.my/abstract/document/9433563 https://doi.org/10.1109/ACCESS.2021.3081629
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spelling	my.ums.eprints.299692021-07-14T08:10:32Z https://eprints.ums.edu.my/id/eprint/29969/ Detection and classification of conflict flows in SDN using machine learning algorithms Mutaz Hamed Hussien Khairi Sharifah Hafizah Syed Ariffin Nurul Mu'azzah Abdul Latiff Kamaludin Mohamad Yusof Mohamed Khalafalla Hassan Fahad Taha Al-Dhief Mosab Hamda Suleman Khan Muzaffar Hamzah QA Mathematics TJ Mechanical engineering and machinery Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN. However, SDN can be influenced by several types of conflicting flows which may lead to deterioration in network performance in terms of efficiency and optimisation. Besides, SDN conflicts occur due to the impact and adjustment of certain features such as priority and action. Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. The experimental results of the detection of conflict flows show that the DT and SVM algorithms achieve accuracies of 99.27% and 98.53% respectively while the EFDT and hybrid DT-SVM algorithms achieve respective accuracies of 99.49% and 99.27%. In addition, the proposed EFDT algorithm achieves 95.73% accuracy on the task of classification between conflict flow types. The proposed EFDT and hybrid DT-SVM algorithms show a high capability of SDN applications to offer fast detection and classification of conflict flows Institute of Electrical and Electronics Engineers Inc. 2021-05-17 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/29969/1/Detection%20and%20classification%20of%20conflict%20flows%20in%20SDN%20using%20machine%20learning%20algorithms-Abstract.pdf text en https://eprints.ums.edu.my/id/eprint/29969/2/Detection%20and%20classification%20of%20conflict%20flows%20in%20SDN%20using%20machine%20learning%20algorithms.pdf Mutaz Hamed Hussien Khairi and Sharifah Hafizah Syed Ariffin and Nurul Mu'azzah Abdul Latiff and Kamaludin Mohamad Yusof and Mohamed Khalafalla Hassan and Fahad Taha Al-Dhief and Mosab Hamda and Suleman Khan and Muzaffar Hamzah (2021) Detection and classification of conflict flows in SDN using machine learning algorithms. IEEE Access, 9. pp. 76024-76037. ISSN 2169-3536 https://ieeexplore-ieee-org.ezproxy.ums.edu.my/abstract/document/9433563 https://doi.org/10.1109/ACCESS.2021.3081629
institution	Universiti Malaysia Sabah
building	UMS Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sabah
content_source	UMS Institutional Repository
url_provider	http://eprints.ums.edu.my/
language	English English
topic	QA Mathematics TJ Mechanical engineering and machinery
spellingShingle	QA Mathematics TJ Mechanical engineering and machinery Mutaz Hamed Hussien Khairi Sharifah Hafizah Syed Ariffin Nurul Mu'azzah Abdul Latiff Kamaludin Mohamad Yusof Mohamed Khalafalla Hassan Fahad Taha Al-Dhief Mosab Hamda Suleman Khan Muzaffar Hamzah Detection and classification of conflict flows in SDN using machine learning algorithms
description	Software-Defined Networking (SDN) is a new type of technology that embraces high flexibility and adaptability. The applications in SDN have the ability to manage and control networks while ensuring load balancing, access control, and routing. These are considered the most significant benefits of SDN. However, SDN can be influenced by several types of conflicting flows which may lead to deterioration in network performance in terms of efficiency and optimisation. Besides, SDN conflicts occur due to the impact and adjustment of certain features such as priority and action. Moreover, applying machine learning algorithms in the identification and classification of conflicting flows has limitations. As a result, this paper presents several machine learning algorithms that include Decision Tree (DT), Support Vector Machine (SVM), Extremely Fast Decision Tree (EFDT) and Hybrid (DT-SVM) for detecting and classifying conflicting flows in SDNs. The EFDT and hybrid DT-SVM algorithms were designed and deployed based on DT and SVM algorithms to achieve improved performance. Using a range flows from 1000 to 100000 with an increment of 10000 flows per step in two network topologies namely, Fat Tree and Simple Tree Topologies, that were created using the Mininet simulator and connected to the Ryu controller, the performance of the proposed algorithms was evaluated for efficiency and effectiveness across a variety of evaluation metrics. The experimental results of the detection of conflict flows show that the DT and SVM algorithms achieve accuracies of 99.27% and 98.53% respectively while the EFDT and hybrid DT-SVM algorithms achieve respective accuracies of 99.49% and 99.27%. In addition, the proposed EFDT algorithm achieves 95.73% accuracy on the task of classification between conflict flow types. The proposed EFDT and hybrid DT-SVM algorithms show a high capability of SDN applications to offer fast detection and classification of conflict flows
format	Article
author	Mutaz Hamed Hussien Khairi Sharifah Hafizah Syed Ariffin Nurul Mu'azzah Abdul Latiff Kamaludin Mohamad Yusof Mohamed Khalafalla Hassan Fahad Taha Al-Dhief Mosab Hamda Suleman Khan Muzaffar Hamzah
author_facet	Mutaz Hamed Hussien Khairi Sharifah Hafizah Syed Ariffin Nurul Mu'azzah Abdul Latiff Kamaludin Mohamad Yusof Mohamed Khalafalla Hassan Fahad Taha Al-Dhief Mosab Hamda Suleman Khan Muzaffar Hamzah
author_sort	Mutaz Hamed Hussien Khairi
title	Detection and classification of conflict flows in SDN using machine learning algorithms
title_short	Detection and classification of conflict flows in SDN using machine learning algorithms
title_full	Detection and classification of conflict flows in SDN using machine learning algorithms
title_fullStr	Detection and classification of conflict flows in SDN using machine learning algorithms
title_full_unstemmed	Detection and classification of conflict flows in SDN using machine learning algorithms
title_sort	detection and classification of conflict flows in sdn using machine learning algorithms
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2021
url	https://eprints.ums.edu.my/id/eprint/29969/1/Detection%20and%20classification%20of%20conflict%20flows%20in%20SDN%20using%20machine%20learning%20algorithms-Abstract.pdf https://eprints.ums.edu.my/id/eprint/29969/2/Detection%20and%20classification%20of%20conflict%20flows%20in%20SDN%20using%20machine%20learning%20algorithms.pdf https://eprints.ums.edu.my/id/eprint/29969/ https://ieeexplore-ieee-org.ezproxy.ums.edu.my/abstract/document/9433563 https://doi.org/10.1109/ACCESS.2021.3081629
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score	13.211869

Detection and classification of conflict flows in SDN using machine learning algorithms

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