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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2021-09-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2021411?viewType=HTML |
| _version_ | 1828951890773147648 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T06:44:28Z |
| format | Article |
| id | doaj.art-52e5058deb234d859ac9af8f4e84893e |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-14T06:44:28Z |
| publishDate | 2021-09-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-52e5058deb234d859ac9af8f4e84893e2022-12-21T23:13:06ZengAIMS PressMathematical Biosciences and Engineering1551-00182021-09-011868298831310.3934/mbe.2021411Securing industrial communication with software-defined networkingAbhishek Savaliya0Rutvij H. Jhaveri1Qin Xin2Saad Alqithami3Sagar Ramani4Tariq Ahamed Ahanger51. Department of Computer Science and Engineering, Pandit Deendayal Energy University, India1. Department of Computer Science and Engineering, Pandit Deendayal Energy University, India2. Faculty of Science and Technology University of the Faroe Islands Vestarabryggja 15, FO 100 Torshavn, Faroe Islands, Denmark3. Department of Computer Science, Albaha University, Saudi Arabia4. A V Parekh Technical institute, Rajkot, India5. College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Saudi ArabiaIndustrial 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.https://www.aimspress.com/article/doi/10.3934/mbe.2021411?viewType=HTMLindustrial cyber-physical systemsmachine learningsoftware-defined networkingnetwork security |
| spellingShingle | Abhishek Savaliya Rutvij H. Jhaveri Qin Xin Saad Alqithami Sagar Ramani Tariq Ahamed Ahanger Securing industrial communication with software-defined networking Mathematical Biosciences and Engineering industrial cyber-physical systems machine learning software-defined networking network security |
| title | 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_short | Securing industrial communication with software-defined networking |
| title_sort | securing industrial communication with software defined networking |
| topic | industrial cyber-physical systems machine learning software-defined networking network security |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2021411?viewType=HTML |
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