This systematic literature review examines 31 peer-reviewed articles released in 2021–2025. It offers a coherent summary of intrusion detection and prevention systems based on machine learning and deep learning of industrial communication protocols. The review categorizes the studies depending on research focus, experimental setup, datasets, and analytical methods. According to the quantitative analysis results, the most suitable model for use in this case is the hybrid deep learning architecture, which includes the combination of Transformer-LSTM models and MODLSTM models, with 29% of the reviewed studies using these models and achieving detection rates of over 99%. Federated learning was mentioned in about 9.7% of the studies, and for 67% of them, real-world data was not available, indicating a lack of access to real-world data. These models are prevalently implemented to identify Denial-of-Service, Man-in-the-Middle, and data injection attacks. The results show that Modbus/TCP is the most studied protocol, which indicates how common it is in industrial systems. Meanwhile, other more recent protocols like MQTT and OPC UA are gaining momentum. Another insight revealed by this review is the tendency towards the use of more realistic validation techniques. Hardware-in-the-loop simulations and physical testbeds are in use in many studies. Integrated solutions which comprise a combination of edge, fog, and cloud computing are gaining popularity. Federated learning (utilized in 6.45% of the selected corpus) and software-defined networking are two emerging directions. Although these developments have taken place, there are still critical gaps, including the scarcity of real-world datasets combined with a lack of robust approaches to address scalability and privacy complications. Furthermore, recent IIoT protocols have not been thoroughly evaluated. The study highlights the need for adaptive and lightweight frameworks and the importance of implementing mechanisms that ensure privacy. There is also a need to have standardized evaluation criteria. These factors combined are instrumental for creating secure, resilient, and interoperable industrial networks during the Industry 4.0 period.

Choosing the right and effective way to assess students is one of the most important tasks of higher education. Many studies have shown that students tend to receive higher scores during their studies when assessed by different study methods which include units that are fully assessed by varying the duration of study or a combination of courses and exams than by exams alone. Many Educational Data Mining studies process data in advance through traditional data extraction, including the data preparation process. In this paper, we propose a different data preparation process by investigating more than 230000 student records for the preparation of scores. The data have been processed through diverse stages in order to extract a categorical factor through which students module marks are refined during the data preparation stage. The results of this work show that students final marks should not be isolated from the nature of the enrolled module assessment methods. They must rather be investigated thoroughly and considered during EDM data preprocessing stage. More generally, educational data should not be prepared in the same way normal data are due to the differences in data sources, applications, and error types. The effect of Module Assessment Index on the prediction process using Random Forest and Naive Bayes classification techniques were investigated. It was shown that considering MAI as attribute increases the accuracy of predicting students second year averages based on their first year averages.