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

Hani Alshahrani, Attiya Khan, Muhammad Rizwan, Mana Saleh Al Reshan (), Adel Sulaiman and Asadullah Shaikh
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Hani Alshahrani: Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Attiya Khan: Department of Computer Science, Kinnaird College for Women, Lahore 54890, Pakistan
Muhammad Rizwan: College of Engineering and Technology, University of Derby, Derby DE22 3AW, UK
Mana Saleh Al Reshan: Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Adel Sulaiman: Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Asadullah Shaikh: Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia

Sustainability, 2023, vol. 15, issue 11, 1-18

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%.

Keywords: industrial internet of things (IIoT); software-defined network; intrusion detection; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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