Securing industrial communication with software-defined networking

Industrial Cyber-Physical Systems (CPSs) require flexible and tolerant communication networks to overcome commonly occurring security problems and denial-of-service such as links failure and networks congestion that might be due to direct or indirect network attacks. In this work, we take advantage...

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Autores principales:	Abhishek Savaliya, Rutvij H. Jhaveri, Qin Xin, Saad Alqithami, Sagar Ramani, Tariq Ahamed Ahanger
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	industrial cyber-physical systems machine learning software-defined networking network security Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/52e5058deb234d859ac9af8f4e84893e
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id	oai:doaj.org-article:52e5058deb234d859ac9af8f4e84893e
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spelling	oai:doaj.org-article:52e5058deb234d859ac9af8f4e84893e2021-11-24T01:13:38ZSecuring industrial communication with software-defined networking10.3934/mbe.20214111551-0018https://doaj.org/article/52e5058deb234d859ac9af8f4e84893e2021-09-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021411?viewType=HTMLhttps://doaj.org/toc/1551-0018Industrial Cyber-Physical Systems (CPSs) require flexible and tolerant communication networks to overcome commonly occurring security problems and denial-of-service such as links failure and networks congestion that might be due to direct or indirect network attacks. In this work, we take advantage of Software-defined networking (SDN) as an important networking paradigm that provide real-time fault resilience since it is capable of global network visibility and programmability. We consider OpenFlow as an SDN protocol that enables interaction between the SDN controller and forwarding plane of network devices. We employ multiple machine learning algorithms to enhance the decision making in the SDN controller. Integrating machine learning with network resilience solutions can effectively address the challenge of predicting and classifying network traffic and thus, providing real-time network resilience and higher security level. The aim is to address network resilience by proposing an intelligent recommender system that recommends paths in real-time based on predicting link failures and network congestions. We use statistical data of the network such as link propagation delay, the number of packets/bytes received and transmitted by each OpenFlow switch on a specific port. Different state-of-art machine learning models has been implemented such as logistic regression, K-nearest neighbors, support vector machine, and decision tree to train these models in normal state, links failure and congestion conditions. The models are evaluated on the Mininet emulation testbed and provide accuracies ranging from around 91–99% on the test data. The machine learning model with the highest accuracy is utilized in the intelligent recommender system of the SDN controller which helps in selecting resilient paths to achieve a better security and quality-of-service in the network. This real-time recommender system helps the controller to take reactive measures to improve network resilience and security by avoiding faulty paths during path discovery and establishment.Abhishek SavaliyaRutvij H. JhaveriQin XinSaad AlqithamiSagar RamaniTariq Ahamed AhangerAIMS Pressarticleindustrial cyber-physical systemsmachine learningsoftware-defined networkingnetwork securityBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 8298-8313 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	industrial cyber-physical systems machine learning software-defined networking network security Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	industrial cyber-physical systems machine learning software-defined networking network security Biotechnology TP248.13-248.65 Mathematics QA1-939 Abhishek Savaliya Rutvij H. Jhaveri Qin Xin Saad Alqithami Sagar Ramani Tariq Ahamed Ahanger Securing industrial communication with software-defined networking
description	Industrial Cyber-Physical Systems (CPSs) require flexible and tolerant communication networks to overcome commonly occurring security problems and denial-of-service such as links failure and networks congestion that might be due to direct or indirect network attacks. In this work, we take advantage of Software-defined networking (SDN) as an important networking paradigm that provide real-time fault resilience since it is capable of global network visibility and programmability. We consider OpenFlow as an SDN protocol that enables interaction between the SDN controller and forwarding plane of network devices. We employ multiple machine learning algorithms to enhance the decision making in the SDN controller. Integrating machine learning with network resilience solutions can effectively address the challenge of predicting and classifying network traffic and thus, providing real-time network resilience and higher security level. The aim is to address network resilience by proposing an intelligent recommender system that recommends paths in real-time based on predicting link failures and network congestions. We use statistical data of the network such as link propagation delay, the number of packets/bytes received and transmitted by each OpenFlow switch on a specific port. Different state-of-art machine learning models has been implemented such as logistic regression, K-nearest neighbors, support vector machine, and decision tree to train these models in normal state, links failure and congestion conditions. The models are evaluated on the Mininet emulation testbed and provide accuracies ranging from around 91–99% on the test data. The machine learning model with the highest accuracy is utilized in the intelligent recommender system of the SDN controller which helps in selecting resilient paths to achieve a better security and quality-of-service in the network. This real-time recommender system helps the controller to take reactive measures to improve network resilience and security by avoiding faulty paths during path discovery and establishment.
format	article
author	Abhishek Savaliya Rutvij H. Jhaveri Qin Xin Saad Alqithami Sagar Ramani Tariq Ahamed Ahanger
author_facet	Abhishek Savaliya Rutvij H. Jhaveri Qin Xin Saad Alqithami Sagar Ramani Tariq Ahamed Ahanger
author_sort	Abhishek Savaliya
title	Securing industrial communication with software-defined networking
title_short	Securing industrial communication with software-defined networking
title_full	Securing industrial communication with software-defined networking
title_fullStr	Securing industrial communication with software-defined networking
title_full_unstemmed	Securing industrial communication with software-defined networking
title_sort	securing industrial communication with software-defined networking
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/52e5058deb234d859ac9af8f4e84893e
work_keys_str_mv	AT abhisheksavaliya securingindustrialcommunicationwithsoftwaredefinednetworking AT rutvijhjhaveri securingindustrialcommunicationwithsoftwaredefinednetworking AT qinxin securingindustrialcommunicationwithsoftwaredefinednetworking AT saadalqithami securingindustrialcommunicationwithsoftwaredefinednetworking AT sagarramani securingindustrialcommunicationwithsoftwaredefinednetworking AT tariqahamedahanger securingindustrialcommunicationwithsoftwaredefinednetworking
_version_	1718416032050184192

Securing industrial communication with software-defined networking

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