Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network

Journal article


Alshahrani, H., Khan, A., Rizwan, M., Al Reshan, M. S., Sulaiman, A. and Shaikh, A. 2023. Intrusion Detection Framework for Industrial Internet of Things Using Software Defined Network. Sustainability. 15 (11), pp. 1-18. https://doi.org/10.3390/su15119001
AuthorsAlshahrani, H., Khan, A., Rizwan, M., Al Reshan, M. S., Sulaiman, A. and Shaikh, A.
Abstract

The Industrial Internet of Things (IIoT) refers to the employment of the Internet of Things in industrial management, where a substantial number of machines and devices are linked and synchronized with the help of software programs and third platforms to improve the overall productivity. The acquisition of the industrial IoT provides benefits that range from automation and optimization to eliminating manual processes and improving overall efficiencies, but security remains to be forethought. The absence of reliable security mechanisms and the magnitude of security features are significant obstacles to enhancing IIoT security. Over the last few years, alarming attacks have been witnessed utilizing the vulnerabilities of the IIoT network devices. Moreover, the attackers can also sink deep into the network by using the relationships amidst the vulnerabilities. Such network security threats cause industries and businesses to suffer financial losses, reputational damage, and theft of important information. This paper proposes an SDN-based framework using machine learning techniques for intrusion detection in an industrial IoT environment. SDN is an approach that enables the network to be centrally and intelligently controlled through software applications. In our framework, the SDN controller employs a machine-learning algorithm to monitor the behavior of industrial IoT devices and networks by analyzing traffic flow data and ultimately determining the flow rules for SDN switches. We use SVM and Decision Tree classification models to analyze our framework’s network intrusion and attack detection performance. The results indicate that the proposed framework can detect attacks in industrial IoT networks and devices with an accuracy of 99.7%.

Keywordsindustrial internet of things (IIoT); software-defined network; intrusion detection; machine learning
Year2023
JournalSustainability
Journal citation15 (11), pp. 1-18
PublisherMDPI Open Access Journals
ISSN 2071-1050
Digital Object Identifier (DOI)https://doi.org/10.3390/su15119001
Web address (URL)https://www.mdpi.com/2071-1050/15/11/9001
Accepted author manuscript
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Open
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online02 Jun 2023
Publication process dates
Accepted31 May 2023
Deposited22 Jun 2023
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https://repository.derby.ac.uk/item/9z38x/intrusion-detection-framework-for-industrial-internet-of-things-using-software-defined-network

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