Industrial intrusion detection based on the behavior of rotating machine

In this study, a new industrial intrusion detection method is introduced for the control system of rotating machines as critical assets in many industries. Data tampering is a major attack on the control systems which disrupts the functionality of the asset. Hence, our objective is to detect data manipulations in the system. We use the behavior of the rotating machine to propose new industrial intrusion detection for the control system of the rotating machine by machine learning techniques. The behavior is elicited by the data of sensors under all the conditions of the rotating machine operation. In this work, the nonlinear regression, novelty detection, outlier detection, and classification approaches are implemented to create behavioral model. On each implementation, online data are compared with the real data of behavior prediction model during the operation of the rotating machine to detect any abnormality. According to our experimental results, the accuracy of the behavioral models created by the One-classSVM novelty detection, k- Nearest Neighbor (kNN) outlier detection, decision tree classifier, k-Neighbors classifier, random forest classifier, and AdaBoost classifier is obtained as 0.98, 0.994, 0.999, 0.999, 0.999, and 0.999, respectively. The results indicate that the proposed industrial intrusion detection method is able to detect the data tampering attacks on the control system of the rotating machines very accurately.