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Machine learning algorithms have been shown to be suitable for securingplatforms for IT systems. However, due to the fundamental differences betweenthe industrial internet of things (IIoT) and regular IT networks, a specialperformance review needs to be considered. The vulnerabilities and securityrequirements of IIoT systems demand different considerations. In this paper, westudy the reasons why machine learning must be integrated into the securitymechanisms of the IIoT, and where it currently falls short in having asatisfactory performance. The challenges and real-world considerationsassociated with this matter are studied in our experimental design. We use anIIoT testbed resembling a real industrial plant to show our proof of concept.

Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning