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Smart manufacturing environments are revolutionizing the industrial sector by integrating advanced technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), and robotics, to achieve higher levels of efficiency, productivity, and safety. However, the increasing complexity and interconnectedness of these systems also introduce new security challenges that must be addressed to ensure the safety of human workers and the integrity of manufacturing processes. Key topics include risk assessment methodologies, secure communication protocols, and the development of standard specifications to guide the design and implementation of HCPS. Recent research highlights the importance of adopting a multi-layered approach to security, encompassing physical, network, and application layers. Furthermore, the integration of AI and machine learning techniques enables real-time monitoring and analysis of system vulnerabilities, as well as the development of adaptive security measures.
Artificial Intelligence Solutions for Cyber-Physical Systems discusses such best practices and frameworks as NIST Cybersecurity Framework, ISO/IEC 27001, and IEC 62443 of advanced technologies. It presents strategies and methods to mitigate risks and enhance security, including cybersecurity frameworks, secure communication protocols, and access control measures. The book also focuses on the design, implementation, and management of secure HCPS in smart manufacturing environments. It covers a wide range of topics, including risk assessment, security architecture, data privacy, and standard specifications, for HCPS. The book highlights the importance of securing communication protocols, the role of Artificial Intelligence and Machine Learning in threat detection and mitigation, and the need for robust cybersecurity frameworks in the context of smart manufacturing.
Cyber-physical systems (CPS) are systems that tightly integrate physical components with computational and networking elements. They are becoming increasingly prevalent in a wide range of applications, such as transportation, healthcare, and manufacturing. Artificial Intelligence (AI) has the potential to significantly improve the performance and capabilities of CPS. For example, AI may be used to automate jobs that are now done by humans, increase the efficiency and accuracy of ­decision?making, and more. Hence, the motivation is to explore that the synergy lies in understanding and harnessing the unprecedented potential it presents. As AI evolves to exhibit more human? like cognitive abilities, CPS seamlessly merges the digital and physical realms.
The scope of study of the Chapter 1 is to bring new capabilities for monitoring, controlling, and optimizing processes AI has the potential to transform CPS. AI techniques like Machine Learning, Natural Language Processing, and Computer Vision can be used to extract insights from data, produce predictions, and automate tasks. It also covers the fundamentals of AI, the difficulties of integrating AI into CPS, and some of the most significant uses of AI in CPS in the upcoming years as CPS become more complicated and networked, AI is anticipated to play a significant role in CPS.
Deep Learning and neural networks are subfields of AI and Machine Learning (ML) that have gained significant attention and success in recent years. They involve the use of computational models inspired by the structure and function of the human brain to process and analyse complex data. Deep Learning is a subfield of Machine Learning that focuses on employing neural networks with numerous hidden layers to model and solve complex patterns in data. It has had great success in a number of fields, including speech and picture identification, natural language processing, and autonomous driving