Safeguarding Industry 4.0: A Machine Learning Approach for Cyber-Physical Systems Security and Sustainability

Abstract

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.