Abstract
This paper analyzes the dynamics of cyberattacks and fraud techniques using machine learning algorithms for Industrial Internet of Things (IIoT) systems. Special attention is paid to information security issues of digital twins within the framework of the Industry 4.0 concept. Various machine learning techniques, such as logistic regression, random forest, and the nearest neighbor method, are considered for classifying attacks in IIoT systems, taking into account the problem of data imbalance inherent in rare attacks. The use of data balancing and cross-validation methods can improve the accuracy of the models and minimize the number of false positives. The results demonstrate that the random forest model outperforms other methods in terms of accuracy, confirming its effectiveness for IIoT security and digital twin protection. Additionally, feature selection and optimization techniques were explored to enhance model performance further. The study also highlights the importance of real-time threat detection, which is crucial for maintaining the integrity and resilience of IIoT environments. Future research is planned to investigate more sophisticated methods, including deep learning, to improve attack detection and classification in the context of Industry 4.0. The integration of federated learning and adaptive AI-driven security measures may also offer promising solutions to emerging cyber threats.