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The major core infrastructure that is utilized in the complex industrial settings of today is known as SCADA systems, which is an acronym that stands for supervisory control and data acquisition. They keep a close eye on critical processes in a wide range of industries, such as manufacturing, water treatment, and energy transportation, to name just a few of the many sectors that they monitor. Some of the industries that they keep an eye on include. At this point in time, these systems are significantly more vulnerable to intrusions as a consequence of the fact that they are becoming increasingly connected to the networks of organizations and the internet. It is because of the fact that they are becoming increasingly connected to the internet that this trend is occurring. Protocol discrepancies and control signal tampering have been brought to everyone’s attention as two of the most severe hazards that might potentially occur. This information has been brought to everyone’s immediate attention. They are capable of altering the direction in which operations are carried out, causing physical processes to become chaotic, and causing considerable harm. Using labeled datasets that are obtained from SCADA communications, the research presents a comprehensive architecture that is used for the purpose of training supervised models that are able to discern between normal and aberrant communication patterns. This is presented for the goal of training supervised models. During the training process, the supervised models are taught to differentiate between normal and abnormal patterns of communication. This enables the creation of models that are capable of educating themselves using their own knowledge, which is a significant advancement. In order to identify errors at the protocol level as well as unauthorized changes in control signals, the framework makes use of techniques such as Random Forest, Support Vector Machines (SVM), and Gradient Boosting. These techniques are applied in order to discover defects. By utilizing these many ways, this objective can be successfully accomplished. In order to accomplish this specific goal, such methods are applied. These models are trained using real-world datasets, which include traffic from Modbus, DNP3, and IEC 60870-5-104. The training process itself takes place on these datasets. Performing this step is done in order to guarantee that the models are appropriate for the domain in which they are being utilized. A significant amount of attention is paid to the significance of feature engineering in this work.