Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning
This addresses security vulnerabilities in Industrial IoT, but the approach is incremental as it builds on existing methods without introducing new techniques.
The paper investigates the challenges of applying machine learning to secure Industrial IoT systems, highlighting performance gaps and using a real-world testbed to demonstrate a proof of concept.
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.