Automated Secure Computing for Next-Generation Systems


Book Description

AUTOMATED SECURE COMPUTING FOR NEXT-GENERATION SYSTEMS This book provides cutting-edge chapters on machine-empowered solutions for next-generation systems for today’s society. Security is always a primary concern for each application and sector. In the last decade, many techniques and frameworks have been suggested to improve security (data, information, and network). Due to rapid improvements in industry automation, however, systems need to be secured more quickly and efficiently. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost. During implementation, the most difficult challenge is determining how to exploit AI and ML algorithms for improved safe service computation while maintaining the user’s privacy. The robustness of AI and deep learning, as well as the reliability and privacy of data, is an important part of modern computing. It is essential to determine the security issues of using AI to protect systems or ML-based automated intelligent systems. To enforce them in reality, privacy would have to be maintained throughout the implementation process. This book presents groundbreaking applications related to artificial intelligence and machine learning for more stable and privacy-focused computing. By reflecting on the role of machine learning in information, cyber, and data security, Automated Secure Computing for Next-Generation Systems outlines recent developments in the security domain with artificial intelligence, machine learning, and privacy-preserving methods and strategies. To make computation more secure and confidential, the book provides ways to experiment, conceptualize, and theorize about issues that include AI and machine learning for improved security and preserve privacy in next-generation-based automated and intelligent systems. Hence, this book provides a detailed description of the role of AI, ML, etc., in automated and intelligent systems used for solving critical issues in various sectors of modern society. Audience Researchers in information technology, robotics, security, privacy preservation, and data mining. The book is also suitable for postgraduate and upper-level undergraduate students.




Automated Secure Computing for Next-Generation Systems


Book Description

AUTOMATED SECURE COMPUTING FOR NEXT-GENERATION SYSTEMS This book provides cutting-edge chapters on machine-empowered solutions for next-generation systems for today’s society. Security is always a primary concern for each application and sector. In the last decade, many techniques and frameworks have been suggested to improve security (data, information, and network). Due to rapid improvements in industry automation, however, systems need to be secured more quickly and efficiently. It is important to explore the best ways to incorporate the suggested solutions to improve their accuracy while reducing their learning cost. During implementation, the most difficult challenge is determining how to exploit AI and ML algorithms for improved safe service computation while maintaining the user’s privacy. The robustness of AI and deep learning, as well as the reliability and privacy of data, is an important part of modern computing. It is essential to determine the security issues of using AI to protect systems or ML-based automated intelligent systems. To enforce them in reality, privacy would have to be maintained throughout the implementation process. This book presents groundbreaking applications related to artificial intelligence and machine learning for more stable and privacy-focused computing. By reflecting on the role of machine learning in information, cyber, and data security, Automated Secure Computing for Next-Generation Systems outlines recent developments in the security domain with artificial intelligence, machine learning, and privacy-preserving methods and strategies. To make computation more secure and confidential, the book provides ways to experiment, conceptualize, and theorize about issues that include AI and machine learning for improved security and preserve privacy in next-generation-based automated and intelligent systems. Hence, this book provides a detailed description of the role of AI, ML, etc., in automated and intelligent systems used for solving critical issues in various sectors of modern society. Audience Researchers in information technology, robotics, security, privacy preservation, and data mining. The book is also suitable for postgraduate and upper-level undergraduate students.




Transforming the Internet of Things for Next-Generation Smart Systems


Book Description

The internet of things (IoT) has massive potential to transform current business models and enhance human lifestyles. With the current pace of research, IoT will soon find many new horizons to touch. IoT is now providing a base of technological advancement in various realms such as pervasive healthcare, smart homes, smart cities, connected logistics, automated supply chain, manufacturing units, and many more. IoT is also paving the path for the emergence of the digital revolution in industrial technology, termed Industry 4.0. Transforming the Internet of Things for Next-Generation Smart Systems focuses on the internet of things (IoT) and how it is involved in modern day technologies in a variety of domains. The chapters cover IoT in sectors such as agriculture, education, business and management, and computer science applications. The multi-disciplinary view of IoT provided within this book makes it an ideal reference work for IT specialists, technologists, engineers, developers, practitioners, researchers, academicians, and students interested in how IoT will be implemented in the next generation of smart systems and play an integral role in advancing technology in the future.




Digital Twin and Blockchain for Smart Cities


Book Description

The book uniquely explores the fundamentals of blockchain and digital twin technologies and their uses in smart cities. In the previous decade, many governments explored artificial intelligence, digital twin, and blockchain, and their roles in smart cities. This book discusses the convergence of two transformative technologies, digital twin and blockchain, to address urban challenges and propel the development of smarter, more sustainable cities. This convergence empowers cities to create real-time replicas of urban environments (digital twins) and secure, transparent data management (blockchain) to improve city planning, management, and civic services. In this application, the concept of a digital twin involves creating a virtual, data-driven replica of a city or specific urban systems, such as transportation, energy, or infrastructure. This digital twin mirrors the real world, gathering data from various sensors, IoT devices, and other sources to provide a holistic view of the city’s operations. Furthermore, blockchain technology offers a decentralized and tamper-resistant ledger for securely storing and managing data. In the context of smart cities, blockchain can ensure data integrity, privacy, and transparency, enabling trust and collaboration among various stakeholders. This book covers many important topics, including real-time city modeling; data security and the trustworthy storage of sensitive urban data; transparent governance to facilitate accountable governance and decision-making processes in smart cities; improved city services; disaster resilience (by providing insights into vulnerabilities and efficient resource allocation during crises); sustainable urban planning that optimizes resource allocation, reduces energy consumption, and minimizes environmental impact, which fosters sustainable development; citizen engagement; and much more. This book will not only provide information about more efficient, resilient, and sustainable urban environments, but it also empowers citizens to be active participants in shaping the future of their cities. By converging these technologies, cities can overcome existing challenges, encourage innovation, and create more livable, connected, and responsive urban spaces. Audience This book has a wide audience in computer science, artificial intelligence, and information technology as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and government/city policymakers working on smart cities, the circular economy, clean tech investors, urban decision-makers, and environmental professionals.




Revolutionizing Healthcare Systems Through Cloud Computing and IoT


Book Description

The healthcare industry has reached its full capacity due to the outbreak of COVID-19. Its global influence has brought attention to the utmost capabilities and limitations of healthcare facilities worldwide. The Internet of Things (IoT) and cloud services can effectively handle the immense healthcare demands that have never been seen before. The scarcity of healthcare personnel and limited resources necessitate the adoption of emerging technology to bolster healthcare delivery. IoT and cloud computing present ample promise in situations like this, as they may be utilized for monitoring, diagnostics, support, and intelligent decision-making. Revolutionizing Healthcare Systems Through Cloud Computing and IoT explores the concepts of cloud computing-based healthcare systems, IoT-based healthcare systems, and cloud-IoT-based healthcare systems. It delves into the significance and benefits of cloud-IoT-based healthcare systems. Covering topics such as disease screening, smart monitoring, and healthcare policy, this book is an excellent resource for researchers, scientists, engineers, graduate and postgraduate students, healthcare professionals and administrators, educators, and more.







Utilizing Generative AI for Cyber Defense Strategies


Book Description

As cyber threats become increasingly sophisticated, the need for innovative defense strategies becomes urgent. Generative artificial intelligence (AI) offers a revolutionary approach to enhance cybersecurity. By utilizing advanced algorithms, data analysis, and machine learning, generative AI can simulate complex attack scenarios, identify vulnerabilities, and develop proactive defense mechanisms while adapting to modern-day cyber-attacks. AI strengthens current organizational security while offering quick, effective responses to emerging threats. Decisive strategies are needed to integrate generative AI into businesses defense strategies and protect organizations from attacks, secure digital data, and ensure safe business processes. Utilizing Generative AI for Cyber Defense Strategies explores the utilization of generative AI tools in organizational cyber security and defense. Strategies for effective threat detection and mitigation are presented, with an emphasis on deep learning, artificial intelligence, and Internet of Things (IoT) technology. This book covers topics such as cyber security, threat intelligence, and behavior analysis, and is a useful resource for computer engineers, security professionals, business owners, government officials, data analysts, academicians, scientists, and researchers.




Strategic Innovations of AI and ML for E-Commerce Data Security


Book Description

As e-commerce continues to increase in usage and popularity, safeguarding consumers private data becomes critical. Strategic innovations in artificial intelligence and machine learning revolutionize data security by offering advanced tools for threat detection and mitigation. Integrating AI and machine learning into their security solutions will allow businesses to build customer trust and maintain a competitive edge throughout the growing digital landscapes. A thorough examination of cutting-edge innovations in e-commerce data security may ensure security measures keep up with current technological advancements in the industry. Strategic Innovations of AI and ML for E-Commerce Data Security explores practical applications in data security, algorithms, and modelling. It examines solutions for securing e-commerce data, utilizing AI and machine learning for modelling techniques, and navigating complex algorithms. This book covers topics such as data science, threat detection, and cybersecurity, and is a useful resource for computer engineers, data scientists, business owners, academicians, scientists, and researchers.




Human Impact on Security and Privacy: Network and Human Security, Social Media, and Devices


Book Description

In an era defined by rapid technological advancements and an increasingly interconnected world, the challenges and opportunities presented by digitalization demand a new approach. The digital world, characterized by optimized, sustainable, and digitally networked solutions, necessitates the integration of intelligence systems, machine learning, deep learning, blockchain methods, and robust cybersecurity measures. Understanding these complex challenges and adapting the synergistic utilization of cutting-edge technologies are becoming increasingly necessary. Human Impact on Security and Privacy: Network and Human Security, Social Media, and Devices provides a global perspective on current and future trends concerning the integration of intelligent systems with cybersecurity applications. It offers a comprehensive exploration of ethical considerations within the realms of security, artificial intelligence, agriculture, and data science. Covering topics such as the evolving landscape of cybersecurity, social engineering perspectives, and algorithmic transparency, this publication is particularly valuable for researchers, industry professionals, academics, and policymakers in fields such as agriculture, cybersecurity, AI, data science, computer science, and ethics.




Artificial Intelligence-Enabled Digital Twin for Smart Manufacturing


Book Description

An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.