Mohamed Elhoseny - Böcker

E-health applications such as tele-medicine, tele-radiology, tele-ophthalmology, and tele-diagnosis are very promising and have immense potential to improve global healthcare. They can improve access, equity, and quality through the connection of healthcare facilities and healthcare professionals, diminishing geographical and physical barriers. One critical issue, however, is related to the security of data transmission and access to the technologies of medical information. Currently, medical-related identity theft costs billions of dollars each year and altered medical information can put a person's health at risk through misdiagnosis, delayed treatment or incorrect prescriptions. Yet, the use of hand-held devices for storing, accessing, and transmitting medical information is outpacing the privacy and security protections on those devices. Researchers are starting to develop some imperceptible marks to ensure the tamper-proofing, cost effective, and guaranteed originality of the medical records. However, the robustness, security and efficient image archiving and retrieval of medical data information against these cyberattacks is a challenging area for researchers in the field of e-health applications.

Intelligent Data Security Solutions for e-Health Applications focuses on cutting-edge academic and industry-related research in this field, with particular emphasis on interdisciplinary approaches and novel techniques to provide security solutions for smart applications. The book provides an overview of cutting-edge security techniques and ideas to help graduate students, researchers, as well as IT professionals who want to understand the opportunities and challenges of using emerging techniques and algorithms for designing and developing more secure systems and methods for e-health applications.

Investigates new security and privacy requirements related to eHealth technologies and large sets of applications Reviews how the abundance of digital information on system behavior is now being captured, processed, and used to improve and strengthen security and privacy Provides an overview of innovative security techniques which are being developed to ensure the guaranteed authenticity of transmitted, shared or stored data/information

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide.

The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks.

Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies

Applications of Computational Intelligence in Multi-Disciplinary Research provides the readers with a comprehensive handbook for applying the powerful principles, concepts, and algorithms of computational intelligence to a wide spectrum of research cases. The book covers the main approaches used in computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods, all of which can be collectively viewed as soft computing. Other key approaches included are swarm intelligence and artificial immune systems. These approaches provide researchers with powerful tools for analysis and problem-solving when data is incomplete and when the problem under consideration is too complex for standard mathematics and the crisp logic approach of Boolean computing.

Provides an overview of the key methods of computational intelligence, including fuzzy logic, neural networks, evolutionary computation, learning theory, and probabilistic methods Includes case studies and real-world examples of computational intelligence applied in a variety of research topics, including bioinformatics, biomedical engineering, big data analytics, information security, signal processing, machine learning, nanotechnology, and optimization techniques Presents a thorough technical explanation on how computational intelligence is applied that is suitable for a wide range of multidisciplinary and interdisciplinary research

Intelligent Edge Computing for Cyber Physical Applications introduces state-of-the-art research methodologies, tools and techniques, challenges, and solutions with further research opportunities in the area of edge-based cyber-physical systems. The book presents a comprehensive review of recent literature and analysis of different techniques for building edge-based CPS. In addition, it describes how edge-based CPS can be built to seamlessly interact with physical machines for optimal performance, covering various aspects of edge computing architectures for dynamic resource provisioning, mobile edge computing, energy saving scenarios, and different security issues.

Sections feature practical use cases of edge-computing which will help readers understand the workings of edge-based systems in detail, taking into account the need to present intellectual challenges while appealing to a broad readership, including academic researchers, practicing engineers and managers, and graduate students.

Introduces and provides reviews on cyber physical and edge computing systems, with different architectures and models needed to address sustainable solutions to social, environmental and economic applications Presents the different architectures of edge computing for building cyber physical systems with dynamic resource provisioning and security solutions Provides AI based perspectives to edge-based cyber physical systems with different algorithms and AI based security solutions Covers different case studies and applications in detail, with real-life examples and possible challenges that can be encountered Offers perspectives for the design, development and commissioning of intelligent edge-based cyber physical systems

Throughout the industry, financial institutions seek to eliminate cumbersome authentication methods, such as PINs, passwords, and security questions, as these antiquated tactics prove increasingly weak. Thus, many organizations now aim to implement emerging technologies in an effort to validate identities with greater certainty. The near instantaneous nature of online banking, purchases, transactions, and payments puts tremendous pressure on banks to secure their operations and procedures.In order to reduce the risk of human error in financial domains, expert systems are seen to offer a great advantage in big data environments. Besides their efficiency in quantitative analysis such as profitability, banking management, and strategic financial planning, expert systems have successfully treated qualitative issues including financial analysis, investment advisories, and knowledge-based decision support systems. Due to the increase in financial applications’ size, complexity, and number of components, it is no longer practical to anticipate and model all possible interactions and data processing in these applications using the traditional data processing model. The emergence of new research areas is clear evidence of the rise of new demands and requirements of modern real-life applications to be more intelligent.This book provides an exhaustive review of the roles of expert systems within the financial sector, with particular reference to big data environments. In addition, it offers a collection of high-quality research that addresses broad challenges in both theoretical and application aspects of intelligent and expert systems in finance. The book serves to aid the continued efforts of the application of intelligent systems that respond to the problem of big data processing in a smart banking and financial environment.

Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in artificial intelligence (AI), sensor networks and Internet of Things (IoT). This book is intended to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications and their implications on the design of solutions for sensor networks. This text introduces researchers and aspiring academicians to the latest developments and trends in AI applications for sensor networks in a clear and well-organized manner. It is mainly useful for research scholars in sensor networks and AI techniques. In addition, professionals and practitioners working on the design of real-time applications for sensor networks may benefit directly from this book. Moreover, graduate and master’s students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real-time applications. This book is written in a simple and easy language, discussing the fundamentals, which relieves the requirement of having early backgrounds in the field. From this expectation and experience, many libraries will be interested in owning copies of this work.

In the era of the Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such a humongous amount of data, it has now become mandatory to exploit the power of massively parallel architecture for fast computation. Cloud computing provides a cheap source of such a computing framework for a large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade.Applications of Cloud Computing: Approaches and Practices lays a good foundation for the core concepts and principles of cloud computing applications, walking the reader through the fundamental ideas with expert ease. The book progresses on the topics in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into the applications of it.It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.

COVID-19, a novel coronavirus pandemic has disrupted our society in many ways. Digital healthcare innovations are required more than ever before as we have come across myriad challenges during this pandemic. Scientists and developers are learning and fi nding ways to use artifi cial intelligence applications and natural language processing to comprehend and tackle this disease.AI technologies are playing an important role in the response to the COVID-19 pandemic. Experts are using all possible tools to study the virus, diagnose individuals, and analyze the public health impacts. This book is a collection of some of the leading efforts related to AI and COVID-19 focused on fi nding how AI can be helpful in monitoring situations from early warnings, swift emergency responses, and critical decision-making. It discusses the use of machine learning and how it may help to reduce the impacts of this pandemic in conjunction with all other research and strategies going on. The book serves as a technical resource of data analytics and AI applications in tracking infectious diseases. It will serve academics, students, data scientists, medical practitioners, and those involved in managing a global pandemic.Features:• Directs the attention to the smart digital healthcare system in this COVID-19 pandemic• Simulates novel investigations and how they will be beneficial in understanding the pandemic• Analyses the various issues related to computing, AI apps, big data analytic techniques, and predictive scientifi skill gaps• Explains some interesting and diverse types of challenges and data-driven healthcare applications

COVID-19, a novel coronavirus pandemic has disrupted our society in many ways. Digital healthcare innovations are required more than ever before as we have come across myriad challenges during this pandemic. Scientists and developers are learning and fi nding ways to use artifi cial intelligence applications and natural language processing to comprehend and tackle this disease.AI technologies are playing an important role in the response to the COVID-19 pandemic. Experts are using all possible tools to study the virus, diagnose individuals, and analyze the public health impacts. This book is a collection of some of the leading efforts related to AI and COVID-19 focused on fi nding how AI can be helpful in monitoring situations from early warnings, swift emergency responses, and critical decision-making. It discusses the use of machine learning and how it may help to reduce the impacts of this pandemic in conjunction with all other research and strategies going on. The book serves as a technical resource of data analytics and AI applications in tracking infectious diseases. It will serve academics, students, data scientists, medical practitioners, and those involved in managing a global pandemic.Features:• Directs the attention to the smart digital healthcare system in this COVID-19 pandemic• Simulates novel investigations and how they will be beneficial in understanding the pandemic• Analyses the various issues related to computing, AI apps, big data analytic techniques, and predictive scientifi skill gaps• Explains some interesting and diverse types of challenges and data-driven healthcare applications

Most of the business sectors consider the Digital Twin concept as the next big thing in the industry. A current state analysis of their digital counterparts helps in the prediction of the future of physical assets. Organizations obtain better insights on their product performance through the implementation of Digital Twins, and the applications of the technology are frequently in sectors such as manufacturing, automobile, retail, health care, smart cities, industrial IoT, etc. This book explores the latest developments and covers the significant challenges, issues, and advances in Digital Twin Technology. It will be an essential resource for anybody involved in related industries, as well as anybody interested in learning more about this nascent technology.This book includes:The future, present, and past of Digital Twin Technology. Digital twin technologies across the Internet of Drones, which developed various perceptive and autonomous capabilities, towards different control strategies such as object detection, navigation, security, collision avoidance, and backup. These approaches help to deal with the expansive growth of big data solutions. The recent digital twin concept in agriculture, which offers the vertical framing by IoT installation development to enhance the problematic food supply situation. It also allows for significant energy savings practices. It is highly required to overcome those challenges in developing advanced imaging methods of disease detection & prediction to achieve more accuracy in large land areas of crops.The welfare of upcoming archetypes such as digitalization in forensic analysis. The ideas of digital twin have arisen to style the corporeal entity and associated facts reachable software and customers over digital platforms.Wind catchers as earth building: Digital Twins vs. green sustainable architecture.

Most of the business sectors consider the Digital Twin concept as the next big thing in the industry. A current state analysis of their digital counterparts helps in the prediction of the future of physical assets. Organizations obtain better insights on their product performance through the implementation of Digital Twins, and the applications of the technology are frequently in sectors such as manufacturing, automobile, retail, health care, smart cities, industrial IoT, etc. This book explores the latest developments and covers the significant challenges, issues, and advances in Digital Twin Technology. It will be an essential resource for anybody involved in related industries, as well as anybody interested in learning more about this nascent technology.This book includes:The future, present, and past of Digital Twin Technology. Digital twin technologies across the Internet of Drones, which developed various perceptive and autonomous capabilities, towards different control strategies such as object detection, navigation, security, collision avoidance, and backup. These approaches help to deal with the expansive growth of big data solutions. The recent digital twin concept in agriculture, which offers the vertical framing by IoT installation development to enhance the problematic food supply situation. It also allows for significant energy savings practices. It is highly required to overcome those challenges in developing advanced imaging methods of disease detection & prediction to achieve more accuracy in large land areas of crops.The welfare of upcoming archetypes such as digitalization in forensic analysis. The ideas of digital twin have arisen to style the corporeal entity and associated facts reachable software and customers over digital platforms.Wind catchers as earth building: Digital Twins vs. green sustainable architecture.

Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in artificial intelligence (AI), sensor networks and Internet of Things (IoT). This book is intended to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications and their implications on the design of solutions for sensor networks. This text introduces researchers and aspiring academicians to the latest developments and trends in AI applications for sensor networks in a clear and well-organized manner. It is mainly useful for research scholars in sensor networks and AI techniques. In addition, professionals and practitioners working on the design of real-time applications for sensor networks may benefit directly from this book. Moreover, graduate and master’s students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real-time applications. This book is written in a simple and easy language, discussing the fundamentals, which relieves the requirement of having early backgrounds in the field. From this expectation and experience, many libraries will be interested in owning copies of this work.

Throughout the industry, financial institutions seek to eliminate cumbersome authentication methods, such as PINs, passwords, and security questions, as these antiquated tactics prove increasingly weak. Thus, many organizations now aim to implement emerging technologies in an effort to validate identities with greater certainty. The near instantaneous nature of online banking, purchases, transactions, and payments puts tremendous pressure on banks to secure their operations and procedures.In order to reduce the risk of human error in financial domains, expert systems are seen to offer a great advantage in big data environments. Besides their efficiency in quantitative analysis such as profitability, banking management, and strategic financial planning, expert systems have successfully treated qualitative issues including financial analysis, investment advisories, and knowledge-based decision support systems. Due to the increase in financial applications’ size, complexity, and number of components, it is no longer practical to anticipate and model all possible interactions and data processing in these applications using the traditional data processing model. The emergence of new research areas is clear evidence of the rise of new demands and requirements of modern real-life applications to be more intelligent.This book provides an exhaustive review of the roles of expert systems within the financial sector, with particular reference to big data environments. In addition, it offers a collection of high-quality research that addresses broad challenges in both theoretical and application aspects of intelligent and expert systems in finance. The book serves to aid the continued efforts of the application of intelligent systems that respond to the problem of big data processing in a smart banking and financial environment.

In the era of the Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such a humongous amount of data, it has now become mandatory to exploit the power of massively parallel architecture for fast computation. Cloud computing provides a cheap source of such a computing framework for a large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade.Applications of Cloud Computing: Approaches and Practices lays a good foundation for the core concepts and principles of cloud computing applications, walking the reader through the fundamental ideas with expert ease. The book progresses on the topics in a step-by-step manner. It reinforces theory with a full-fledged pedagogy designed to enhance students' understanding and offer them a practical insight into the applications of it.It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.

Metaheuristics Algorithms for Medical Applications: Methods and Applications provides readers with the most complete reference for developing metaheuristics techniques with machine learning for solving biomedical problems. This book is organized to present a stepwise progression beginning with the basics of metaheuristics, leading into methods and practices, and concluding with advanced topics. The first section of this book presents the fundamental concepts of metaheuristics and machine learning and provides a comprehensive taxonomic view of metaheuristics methods according to a variety of criteria such as data type, scope, and method. The second section of this book explains how to apply metaheuristics techniques for solving large-scale biomedical problems, including analysis and validation under different strategies. The final portion of the book focuses on advanced topics in metaheuristics in four different applications. Readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in metaheuristics for biomedical science. This book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in metaheuristics, including mathematics, biomedical engineering, computer science, biological sciences, and clinicians in medical practice.

Introduces a new set of Metaheuristics techniques for biomedical applicationsPresents basic concepts of Metaheuristics, methods and practices, followed by advanced topics and applicationsProvides researchers, practitioners, and project stakeholders with a complete guide for understanding and applying metaheuristics and machine learning techniques in their projects and solutions

Multi-Criteria Decision-Making for Renewable Energy: Methods, Applications, and Challenges brings together the latest fuzzy and soft computing methods, models, and algorithms as applied to the field of renewable energy and supported by specific application examples and case studies. The book begins by approaching renewable energy sources, challenges and factors that affect their development, as well as green renewable energy sites and the utilization of fuzzy multi-criteria decision-making (MCDM) techniques in these broad contexts, as well as utilization in addressing the various environmental, economic, and social barriers to ensuring the sustainability of energy resources.

Detailed chapters focus on the application of multi-criteria decision-making methods for planning, modeling and prioritization in specific areas of renewable energy, including solar energy, wind farms, solar-powered hydrogen production plants, biofuel production, energy storage, hydropower, and marine energy. Finally, future opportunities and research directions are explored.

Provides a set of multi-criteria techniques to address challenges across renewable energy Reviews and analyzes the current state-of-the-art and identifies future opportunities and directions Offers clear examples, case studies and practical applications of the described concepts

The global adoption of technology in education is transforming the way we teach and learn. Artificial Intelligence is one of the disruptive techniques to customize the experience of different learning groups, teachers, and tutors. This book offers knowledge in intelligent teaching/learning systems, and advances in e-learning and assessment systems. The book highlights the broad field of artificial intelligence applications in education, regarding any type of artificial intelligence that is correlated with education. It discusses learning methodologies, intelligent tutoring systems, intelligent student guidance and assessments, intelligent education chatbots, and artificial tutors and presents the practicality and applicability implications of AI in education. The book offers new and current research along with case studies showing the latest techniques and educational activities.The book will find interest with academicians which includes teachers, students of various disciplines, higher education policymakers who believe in transforming the education industry, and research scholars who are pursuing their Ph.D. or Post Doc. in the field of Education Technology, Education, and Learning, etc. and those working in the area of Education Technology and Artificial Intelligence such industry professionals in education management and e-learning companies.

The global adoption of technology in education is transforming the way we teach and learn. Artificial Intelligence is one of the disruptive techniques to customize the experience of different learning groups, teachers, and tutors. This book offers knowledge in intelligent teaching/learning systems, and advances in e-learning and assessment systems. The book highlights the broad field of artificial intelligence applications in education, regarding any type of artificial intelligence that is correlated with education. It discusses learning methodologies, intelligent tutoring systems, intelligent student guidance and assessments, intelligent education chatbots, and artificial tutors and presents the practicality and applicability implications of AI in education. The book offers new and current research along with case studies showing the latest techniques and educational activities.The book will find interest with academicians which includes teachers, students of various disciplines, higher education policymakers who believe in transforming the education industry, and research scholars who are pursuing their Ph.D. or Post Doc. in the field of Education Technology, Education, and Learning, etc. and those working in the area of Education Technology and Artificial Intelligence such industry professionals in education management and e-learning companies.

This book seamlessly connects the topics of Industry 4.0 and cyber security. It discusses the risks and solutions of using cyber security techniques for Industry 4.0.Cyber Security and Operations Management for Industry 4.0 covers the cyber security risks involved in the integration of Industry 4.0 into businesses and highlights the issues and solutions. The book offers the latest theoretical and practical research in the management of cyber security issues common in Industry 4.0 and also discusses the ethical and legal perspectives of incorporating cyber security techniques and applications into the day-to-day functions of an organization. Industrial management topics related to smart factories, operations research, and value chains are also discussed.This book is ideal for industry professionals, researchers, and those in academia who are interested in learning more about how cyber security and Industry 4.0 are related and can work together.

This book seamlessly connects the topics of Industry 4.0 and cyber security. It discusses the risks and solutions of using cyber security techniques for Industry 4.0.Cyber Security and Operations Management for Industry 4.0 covers the cyber security risks involved in the integration of Industry 4.0 into businesses and highlights the issues and solutions. The book offers the latest theoretical and practical research in the management of cyber security issues common in Industry 4.0 and also discusses the ethical and legal perspectives of incorporating cyber security techniques and applications into the day-to-day functions of an organization. Industrial management topics related to smart factories, operations research, and value chains are also discussed.This book is ideal for industry professionals, researchers, and those in academia who are interested in learning more about how cyber security and Industry 4.0 are related and can work together.

There currently is no in-depth book dedicated to the challenge of the Internet of Everything and Big Data technologies in smart cities. Humankind today is confronting a critical worldwide portability challenge and the framework that moves cities must keep pace with the innovation. Internet of Everything and Big Data: Major Challenges in Smart Cities reviews the applications, technologies, standards, and other issues related to smart cities.This book is dedicated to addressing the major challenges in realizing smart cities and sensing platforms in the era of Big Data cities and Internet of Everything. Challenges vary from cost and energy efficiency to availability and service quality. This book examines security issues and challenges, addresses the total information science challenges, covers exploring and creating IoT environment-related sales adaptive systems, and investigates basic and high-level concepts using the latest techniques implemented by researchers and businesses.The book is written for analysts, researchers, and specialists who are working on the future generation of the technologies. It will serve as a valuable guide for those in the industry, and students as well.

This book offers an essential guide to IoT Security, Smart Cities, IoT Applications, etc. In addition, it presents a structured introduction to the subject of destination marketing and an exhaustive review on the challenges of information security in smart and intelligent applications, especially for IoT and big data contexts. Highlighting the latest research on security in smart cities, it addresses essential models, applications, and challenges.Written in plain and straightforward language, the book offers a self-contained resource for readers with no prior background in the field. Primarily intended for students in Information Security and IoT applications (including smart cities systems and data heterogeneity), it will also greatly benefit academic researchers, IT professionals, policymakers and legislators. It is well suited as a reference book for both undergraduate and graduate courses on information security approaches, the Internet of Things, andreal-world intelligent applications.

This book provides a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of dynamic wireless sensor networks (WSNs) for intelligent and smart applications in a variety of environments. It presents the most central concepts associated with Dynamic Wireless Sensor Networks applications, and discusses issues surrounding Wireless Sensor Network Structures for complex and mobile-based applications. The book subsequently discusses several topics related to energy management in dynamic WSNs, and reviews the steps involved in building a secure and trusted data transmission model using the WSN applications of intelligent environments. Lastly, it discusses the applications of WSNs in live data systems such as SCADA systems. Readers will discover a collection of high-quality research works that address broad challenges in both theoretical and applied aspects of WSNs for intelligent real-life applications. In addition, the book presents original research on the application of a dynamic WSN to solve the problem of energy consumption in a secure WSN during the process of data aggregation and transmission. Written by respected experts in the field, the book will stimulate further efforts in the application of the intelligent WSNs model, helping to solve the problem of data processing in a limited resource WSN-based environment.

This book offers an essential guide to IoT Security, Smart Cities, IoT Applications, etc. Primarily intended for students in Information Security and IoT applications (including smart cities systems and data heterogeneity), it will also greatly benefit academic researchers, IT professionals, policymakers and legislators.

This book provides a concise overview of the current state of the art in cybersecurity and shares novel and exciting ideas and techniques, along with specific cases demonstrating their practical application. It gathers contributions by both academic and industrial researchers, covering all aspects of cybersecurity and addressing issues in secure information systems as well as other emerging areas.  The content comprises high-quality research articles and reviews that promote a multidisciplinary approach and reflect the latest advances, challenges, requirements and methodologies. Thus, the book investigates e.g. security vulnerabilities, cybercrime, and privacy issues related to big data analysis, as well as advances in digital forensics, secure smart city services, and risk mitigation strategies for devices employing cyber-physical systems.  Given its scope, the book offers a valuable resource for students, researchers, IT professionals and providers, citizens, consumers and policymakers involved or interested in the modern security procedures needed to protect our information and communication resources. Its goal is to foster a community committed to further research and education, and one that can also translate its findings into concrete practices.

Given its scope, the book offers a valuable resource for students, researchers, IT professionals and providers, citizens, consumers and policymakers involved or interested in the modern security procedures needed to protect our information and communication resources.