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This new book discusses the concepts while also highlighting the challenges in the field of quantum cryptography and also covering cryptographic techniques and cyber security techniques, in a single volume. It comprehensively covers important topics in the field of quantum cryptography with applications, including quantum key distribution, position-based quantum cryptography, quantum teleportation, quantum e-commerce, quantum cloning, cyber security techniques’ architectures and design, cyber security techniques management, software-defined networks, and cyber security techniques for 5G communication. The text also discusses the security of practical quantum key distribution systems, applications and algorithms developed for quantum cryptography, as well as cyber security through quantum computing and quantum cryptography. The text will be beneficial for graduate students, academic researchers, and professionals working in the fields of electrical engineering, electronics and communications engineering, computer science, and information technology.
The text provides readers with an overview of cloud computing, beginning with historical perspectives on mainframe computers and early networking protocols, moving to current issues such as security of hardware and networks, performance, evolving IoT areas, edge computing, etc.
With the increasing power of computing, cybersecurity faces mounting threats, making digital systems more vulnerable to attacks. While modern cryptography used to be compelling, it now shows vulnerabilities against rapidly growing computational capabilities. Therefore, robust security solutions have become urgent in this precarious landscape. Advancing Cyber Security Through Quantum Cryptography is a book that can guide us through the turbulent waters of cybersecurity and quantum cryptography. It offers a panoramic view of current affairs, insightful analyses, illuminating case studies, and meticulous exploration of challenges and opportunities. Through this book, readers can gain knowledge and navigate this complex terrain. It delves into critical areas where quantum cryptography can fortify cybersecurity defenses, such as secure communications, e-commerce, and quantum internet.
In an age of explosive worldwide growth of electronic data storage and communications, effective protection of information has become a critical requirement. When used in coordination with other tools for ensuring information security, cryptography in all of its applications, including data confidentiality, data integrity, and user authentication, is a most powerful tool for protecting information. This book presents a collection of research work in the field of cryptography. It discusses some of the critical challenges that are being faced by the current computing world and also describes some mechanisms to defend against these challenges. It is a valuable source of knowledge for researchers, engineers, graduate and doctoral students working in the field of cryptography. It will also be useful for faculty members of graduate schools and universities.
The shortcomings of modern cryptography and its weaknesses against computers that are becoming more powerful necessitate serious consideration of more robust security options. Quantum cryptography is sound, and its practical implementations are becoming more mature. Many applications can use quantum cryptography as a backbone, including key distribution, secure direct communications, large prime factorization, e-commerce, e-governance, quantum internet, and more. For this reason, quantum cryptography is gaining interest and importance among computer and security professionals. Quantum Cryptography and the Future of Cyber Security is an essential scholarly resource that provides the latest research and advancements in cryptography and cyber security through quantum applications. Highlighting a wide range of topics such as e-commerce, machine learning, and privacy, this book is ideal for security analysts, systems engineers, software security engineers, data scientists, vulnerability analysts, professionals, academicians, researchers, security professionals, policymakers, and students.
Computational Intelligence in Urban Infrastructure consolidates experiences and research results in computational intelligence and its applications in urban infrastructure. It discusses various techniques and application areas of smart urban infrastructure including topics related to smart city management. Major topics covered include smart home automation, intelligent lighting, smart human care services, intelligent transportation systems, ontologies in urban development domain, and intelligent monitoring, control, and security of critical infrastructure systems supported by case studies. Features: Covers application of AI and computational intelligence techniques in urban infrastructure planning Discusses characteristics and features of smart urban management Explores relationship between smart home and smart city management Deliberates various smart home techniques Includes different case studies for supporting and analyzing various aspects of smart urban infrastructure management This book is aimed at researchers, graduate students, libraries in communication networks, urban and town planning, and civil engineering.
This book presents the role of AI-Driven Digital Twin in the Industry 4.0 ecosystem by focusing on Smart Manufacturing, sustainable development, and many other applications. It also discusses different case studies and presents an in-depth understanding of the benefits and limitations of using AI and Digital Twin for industrial developments. AI-Driven Digital Twin and Industry 4.0: A Conceptual Framework with Applications introduces the role of Digital Twin in Smart Manufacturing and focuses on the Digital Twin framework throughout. It provides a summary of the various AI applications in the Industry 4.0 environment and emphasizes the role of advanced computational and communication technologies. The book offers demonstrative examples of AI-Driven Digital Twin in various application domains and includes AI techniques used to analyze the environmental impact of industrial operations along with examples. The book reviews the major challenges in the deployment of AI-Driven Digital Twin in the Industry 4.0 ecosystem and presents an understanding of how AI is used in the designing of Digital Twin for various applications. The book also enables familiarity with various industrial applications of computational and communication technologies and summarizes the ongoing research and innovations in the areas of AI, Digital Twin, and Smart Manufacturing while also tracking the various research challenges along with future advances. This reference book is a must-read and is very beneficial to students, researchers, academicians, industry experts, and professionals working in related fields.
Blockchain and distributed-ledger technologies enable new modes of communication, synchronization, and transfer of value with a broad impact on Internet of Things (IoT), Data Science, society, industry, commerce, and government. This book studies the potential impact of blockchain and distributed-ledger technologies on IoT. It highlights the application of possible solutions in the domain of blockchain and IoT system security, including cryptology, distributed systems, law, formal methods, code verification and validation, software, and systems metrics. As the field is growing fast, the book adapts to the changing research landscape, integrates and cross-links studies and citations in related subfields, and provides an overview of these fields and how they complement each other. • Highlights how the security aspect of the integration of blockchain with IoT will help to design secure data solutions for various domains • Offers fundamental knowledge of the blockchain concept and its usage in real-life applications • Presents current and future trends on the IoT and blockchain with an efficient, scalable, and sustainable approach • Reviews future developments in blockchain and IoT in future job opportunities • Discusses how blockchain for IoT systems can help a varied range of end-users to access computational and storage resources This book is intended for postgraduate students and researchers in the departments of computer science, working in the areas of IoT, blockchain, deep learning, machine learning, image processing, and big data.
This book presents a compilation of case studies from practitioners, educators, and researchers working in the area of digital violence, along with methodologies to prevent it using cyber security. The book contains three basic sections namely: the concept of digital violence in policy and practice; the impact of digital violence; and the implication of cyber security to curb such violence. The intention of this book is to equip researchers, practitioners, faculties, and students with critical, practical, and ethical resources to use cyber security and related technologies to help curb digital violence and to support victims. It brings about the needs of technological based education in order to combat gendered crimes like cyberbullying, body-shaming, and trolling that are a regular phenomenon on social media platforms. Topics include societal implications of cyber feminism; technology aided communication in education; cyber security and human rights; governance of cyber law through international laws; and understanding digital violence.
Predictive Modeling in Biomedical Data Mining and Analysis presents major technical advancements and research findings in the field of machine learning in biomedical image and data analysis. The book examines recent technologies and studies in preclinical and clinical practice in computational intelligence. The authors present leading-edge research in the science of processing, analyzing and utilizing all aspects of advanced computational machine learning in biomedical image and data analysis. As the application of machine learning is spreading to a variety of biomedical problems, including automatic image segmentation, image classification, disease classification, fundamental biological processes, and treatments, this is an ideal reference. Machine Learning techniques are used as predictive models for many types of applications, including biomedical applications. These techniques have shown impressive results across a variety of domains in biomedical engineering research. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood, hence the need for new resources and information. - Includes predictive modeling algorithms for both Supervised Learning and Unsupervised Learning for medical diagnosis, data summarization and pattern identification - Offers complete coverage of predictive modeling in biomedical applications, including data visualization, information retrieval, data mining, image pre-processing and segmentation, mathematical models and deep neural networks - Provides readers with leading-edge coverage of biomedical data processing, including high dimension data, data reduction, clinical decision-making, deep machine learning in large data sets, multimodal, multi-task, and transfer learning, as well as machine learning with Internet of Biomedical Things applications