Enhancing Security in HIL-based Augmented Industrial Control Systems: Insights from Dataset Analysis and Model Development

This Paper offers a comprehensive investigation into the enhancement of safety in HIL-based ICS-augmented industrial control systems via the use of data analysis and mode creation to accomplish this. The state-of-the-art security behavior mode for industrial control systems that are based on HIL technology is being investigated using a variety of artificial intelligence algorithms, with recall serving as a performance indicator. The implementation of comparative performance assessments is the first step into unexplored areas for these algorithmic techniques; let's get started! In this part, we take a comprehensive look into the outcomes that occur when Random Forest Logistic Regression KNN approaches are evaluated. When it comes to recall, in addition to various conceivable algorithms that may be used between them. Our findings indicate that the method with the best performance in terms of recall is logistic regression. This is the strategy that we discovered to be the most effective. This demonstrates that it is capable of accurately identifying significant occurrences within the collection. In addition to providing valuable insights regarding the implementation of machine learning approaches to enhance the safety of HIL-based Augmented ICS, the results of this study serve as a foundation for future advancements in this very important field of research.