This book presents advances in security assurance for cyber-physical systems (CPS) and report on new machine learning (ML) and artificial intelligence (AI) approaches and technologies developed by the research community and the industry to address the challenges faced by this emerging field. Cyber-physical systems bridge the divide between cyber and physical-mechanical systems by combining seamlessly software systems, sensors, and actuators connected over computer networks. Through these sensors, data about the physical world can be captured and used for smart autonomous decision-making. This book introduces fundamental AI/ML principles and concepts applied in developing secure and trustworthy CPS, disseminates recent research and development efforts in this fascinating area, and presents relevant case studies, examples, and datasets. We believe that it is a valuable reference for students, instructors, researchers, industry practitioners, and related government agencies staff.

This Open Access proceedings presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Karlsruhe, October 23-24, 2018. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

This book presents the proceedings of the 6th International Conference on Wireless Intelligent and Distributed Environment for Communication (WIDECOM 2023), which took place at Brock University, St. Catharines, Ontario, Canada, October 11-13, 2023. The book addresses issues related to new dependability paradigms, design, and performance of dependable network computing and mobile systems, as well as issues related to the security of these systems. The goal of the conference is to provide a forum for researchers, students, scientists and engineers working in academia and industry to share their experiences, new ideas and research results in the above-mentioned areas.

This book presents the proceedings of the 5th International Conference on Wireless Intelligent and Distributed Environment for Communication (WIDECOM 2022), which took place at the University of Windsor, Windsor, Canada, October 12-14, 2022. The book addresses issues related to new dependability paradigms, design, and performance of dependable network computing and mobile systems, as well as issues related to the security of these systems. The goal of the conference is to provide a forum for researchers, students, scientists and engineers working in academia and industry to share their experiences, new ideas and research results in the above-mentioned areas. Presents the proceedings of the International Conference on Wireless Intelligent and Distributed Environment for Communication (WIDECOM 2022); Includes an array of topics including networking computing, security, mobile/ubiquitous systems, cloud systems, and IoT systems; Addresses issues related to protecting information security and establishing trust in the digital space.

The work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS – Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.

Cyber-physical systems (CPSs) consist of software-controlled computing devices communicating with each other and interacting with the physical world through sensors and actuators. Because most of the functionality of a CPS is implemented in software, the software is of crucial importance for the safety and security of the CPS. This book presents principle-based engineering for the development and operation of dependable software. The knowledge in this book addresses organizations that want to strengthen their methodologies to build safe and secure software for mission-critical cyber-physical systems. The book: • Presents a successful strategy for the management of vulnerabilities, threats, and failures in mission-critical cyber-physical systems; • Offers deep practical insight into principle-based software development (62 principles are introduced and cataloged into five categories: Business & organization, general principles, safety, security, and risk management principles); • Provides direct guidance on architecting and operating dependable cyber-physical systems for software managers and architects.

Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors Includes four case studies addressing real-world industrial problems solved with machine learning techniques A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka