Abstract:
With the rapid expansion of industrial IoT (IIoT), maintaining robust
cybersecurity is essential for the smooth operation of industrial processes. Industrial
environments require adaptive solutions to effectively mitigate evolving cyber
threats and protect sensitive operations. This research aims to improve the
cybersecurity of industrial IoT environments. The research intends to design and
implement an adaptive and real-time intrusion detection system with edge
computing integration that improves the reliability of the operations in industrial
IoT. We incorporated machine learning approaches to classify cyber threats using
XGBoost and Deep Neural Networks (DNN). A comparative analysis of results
obtained from two datasets shows that the XGBoost model was slightly more
accurate than the DNN model, with an accuracy of 79% for dataset D1 and
approximately 99.42% for data set D2. This analysis also clearly demonstrates the
usefulness of these machine learning approaches and the need to select a model
depending on the requirements for detecting particular attacks. Confusion matrix
analysis shows that both models have several advantages in terms of recognizing
different types of cyber threats.
