  Machine learning algorithms have been shown to be suitable for securing
platforms for IT systems. However, due to the fundamental differences between
the industrial internet of things (IIoT) and regular IT networks, a special
performance review needs to be considered. The vulnerabilities and security
requirements of IIoT systems demand different considerations. In this paper, we
study the reasons why machine learning must be integrated into the security
mechanisms of the IIoT, and where it currently falls short in having a
satisfactory performance. The challenges and real-world considerations
associated with this matter are studied in our experimental design. We use an
IIoT testbed resembling a real industrial plant to show our proof of concept.
