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The proliferation of connected objects, with over 8 billion IoT devices currently and a projected increase to 41 billion by 2027, signifies the widespread integration of technology in sectors like Smart City, Industry 4.0, e-commerce, and e-health. This study focuses on the security assessment of Cyber-Physical Systems (CPS) in manufacturing processes, utilizing six supervised algorithms on a dataset with 61 features. The results not only offer valuable insights into security but also contribute to the optimization of machine learning models. This research implicitly addresses the sustainability aspect by acknowledging the broader impact of CPS technologies. Cyber-Physical Systems (CPS) optimization of machine learning models not only fits in with the industry 4.0 framework’s overarching goal of promoting environmentally friendly practices, but it also creates a vital connection between sustainability and the security paradigm that these complex systems are built upon. This mutually beneficial relationship highlights how improving machine learning algorithms with the goal of reducing environmental impact also helps to strengthen the security infrastructure of CPS. Industry 4.0 prioritizes environmental responsibility by emphasizing the development and application of eco-conscious practices. It also acknowledges the interdependence of sustainability and security within the framework of dynamic cyber-physical ecosystems.

The Internet of things (IoT) has evolved significantly, automating daily activities by connecting numerous devices. However, this growth has increased cybersecurity threats, compromising data integrity. To address this, intrusion detection systems (IDSs) have been developed, mainly using predefined attack patterns. With rising cyber-attacks, improving IDS effectiveness is crucial, and machine learning is a key solution. This research enhances IDS capabilities by introducing binary attack identification and multiclass attack categorization for IoT traffic, aiming to improve IDS performance. Our framework uses the 'BoT-IoT' and 'TON-IoT' datasets, which include various IoT network traffic and cyber-attack scenarios, such as DDoS and data infiltration, to train machine learning and ensemble models. Specifically, it combines three machine learning models-decision tree, resilient backpropagation (RProp) multilayer perceptron (MLP), and logistic regression-into ensemble methods like voting and stacking to improve prediction accuracy and reduce detection errors. These ensemble classifiers outperform individual models, demonstrating the benefit of diverse learning techniques. Our framework achieves high accuracy, with 99.99% for binary classification on the BoT-IoT dataset and 97.31% on the ToN-IoT dataset. For multiclass classification, it achieves 99.99% on BoT-IoT and 96.32% on ToN-IoT, significantly enhancing IDS effectiveness against IoT cybersecurity threats. [ABSTRACT FROM AUTHOR]