Securing industrial communication with software-defined networking
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Published:2021
Issue:6
Volume:18
Page:8298-8313
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ISSN:1551-0018
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Container-title:Mathematical Biosciences and Engineering
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language:
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Short-container-title:MBE
Author:
Savaliya Abhishek, ,Jhaveri Rutvij H.,Xin Qin,Alqithami Saad,Ramani Sagar,Ahanger Tariq Ahamed, , , ,
Abstract
<abstract>
<p>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.</p>
</abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Reference28 articles.
1. K. D. Kim, P. R. Kumar, An overview and some challenges in cyber-physical systems, J. Indian Inst. Sci., 93 (2013), 341-352. 2. G. Greenwood, J. Gallagher, E. Matson, Cyber-physical systems: the next generation of evolvable hardware research and applications, in 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, (2015), 285-296. 3. H. He, C. Maple, T. Watson, A. Tiwari, J. Mehnen, Y. Jin, et al., The security challenges in the IoT enabled cyber-physical systems and opportunities for evolutionary computing other computational intelligence, in 2016 IEEE Congress on Evolutionary Computation (CEC), (2015), 1015-1021. 4. H. Toral-Cruz, D. He, A. D. Mihovska, K. K. R. Choo, M. K. Khan, Reliable and Secure e-Health Networks, Wireless Pers. Commun., 117 (2021), 1-6. 5. P. Pandey, R. Litoriya, Securing e-health networks from counterfeit medicine penetration using blockchain, Wireless Pers. Commun., 117 (2021), 7-25.
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