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), Naïve 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.

工业控制系统用于工业过程中，以减轻人为因素负担，并处理复杂的工业系统过程以及它们之间的有效通信。物联网（IoT）是信息网络将设备和传感器融合在一起，以实现新的自主功能。IoT与称为工业物联网（IIoT）的工业应用程序的集成。在一些重要的基础设施中（例如供水网络）可以找到IIoT。如今，ICS很容易使用Internet连接来使工业IoT传感器彼此实时通信。因此，本文提出了一项分析研究，以检测IIoT基础设施中关键水基础设施的网络物理中的异常，恶意活动和网络攻击。该研究使用各种机器学习算法对异常事件进行分类，包括几种攻击和IIoT硬件故障。分析了涵盖正常系统活动的15种异常情况的真实数据集，以对所提出的方法进行研究回顾。测试情况涉及从硬件故障到水SCADA设备破坏的一系列事件。为了对恶意活动进行分类，可以使用各种机器学习方法，例如逻辑回归（LR），线性判别分析（LDA），k最近邻（KNN），朴素贝叶斯（NB），支持向量机（SVM），分类和使用了回归树（CART）。结果表明，CART和NB在准确性，准确性，召回率和F1得分方面均具有最佳结果。