Anomaly events classification and detection system in critical industrial internet of things infrastructure using machine learning algorithms

Abstract

Industrial Control System is used in the industrial process for reducing the human factor burden and handling the complex industrial system process and communications between them efficiently. Internet of Things (IoT) is the fusion of devices and sensors by an information network to enable new and autonomous capabilities. The integration of IoT with industrial applications known as the Industrial Internet of Things (IIoT). The IIoT is found in several critical infrastructures such as water distribution networks. Nowadays, ICS is vulnerable to using the Internet connection to enable industrial IoT sensors to communicate with each other in Real-Time. Therefore, this paper presents an analytical study of detecting anomalies, malicious activities, and cyber-attacks in a cyber-physical of critical water infrastructure in the IIoT infrastructure. The study uses various machine learning algorithms to classify the anomaly events including several attacks and IIoT hardware failures. A real-world dataset covering 15 anomaly situations of normal system activity was analyzed for the research review of the proposed approach. The test situations involved a wide array of incidents from hardware breakdown to water SCADA device sabotage. To classify the malicious activity, various machine learning methods, such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), k-nearest neighbours (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Classification and Regression Tree (CART) are used. The results show that CART and NB have the best results for accuracy, precision, recall, and F1-score.