As a result of its widespread implementation in economic and social structures, the network concept appears to be a paradigm of the contemporary world. The need for various services - transport, energy, consumption of manufacturing goods, provision of care, information and communication, etc. - draws users into interwoven networks which are meshes of material and immaterial flows. In this context, the user is a consumer of goods and services from industries and administrations, or they themselves are part of the organization (digital social networks). This book examines the invariants that unify networks in their diversity, as well as the specificities that differentiate them. It provides a reading grid that distinguishes a generic level where these systems find a common interpretation, and a specific level where appropriate analytical methods are used. Three case studies from different fields are presented to illustrate the purpose of the book in detail.

Wireless sensor networks have gained significant attention industrially and academically due to their wide range of uses in various fields. Because of their vast amount of applications, wireless sensor networks are vulnerable to a variety of security attacks. The protection of wireless sensor networks remains a challenge due to their resource-constrained nature, which is why researchers have begun applying several branches of artificial intelligence to advance the security of these networks. Research is needed on the development of security practices in wireless sensor networks by using smart technologies. Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks provides emerging research exploring the theoretical and practical advancements of security protocols in wireless sensor networks using artificial intelligence-based techniques. Featuring coverage on a broad range of topics such as clustering protocols, intrusion detection, and energy harvesting, this book is ideally designed for researchers, developers, IT professionals, educators, policymakers, practitioners, scientists, theorists, engineers, academicians, and students seeking current research on integrating intelligent techniques into sensor networks for more reliable security practices.

Interest in developing smart cities has grown exponentially over the years with many governments across the world hoping to initiate these projects in their own countries. One of the key challenges for the success of any smart city project is the assurance of smart security and privacy of the citizens. Due to the use of a wide range of interconnected cyber-physical systems, traditional security solutions cannot be applied to smart city applications, and new practices must be sought. Secure Cyber-Physical Systems for Smart Cities is an essential reference publication that examines information security and privacy in smart city settings including discussions on new security frameworks, solutions, cybersecurity laws and regulations, and risk management frameworks for smart city environments. Covering a wide range of topics including wireless networks, security, and cyber-physical systems, this book is ideally designed for IT specialists and consultants, engineers, government officials, policymakers, researchers, academicians, and industry professionals.

With the internet of things (IoT), it is proven that enormous networks can be created to interconnect objects and facilitate daily life in a variety of domains. Research is needed to study how these improvements can be applied in different ways, using different technologies, and through the creation of different applications. IoT Protocols and Applications for Improving Industry, Environment, and Society contains the latest research on the most important areas and challenges in the internet of things and its intersection with technologies and tools such as artificial intelligence, blockchain, model-driven engineering, and cloud computing. The book covers subfields that examine smart homes, smart towns, smart earth, and the industrial internet of things in order to improve daily life, protect the environment, and create safer and easier jobs. While covering a range of topics within IoT including Industry 4.0, security, and privacy, this book is ideal for computer scientists, engineers, practitioners, stakeholders, researchers, academicians, and students who are interested in the latest applications of IoT.

As technology continues to expand and develop, the internet of things (IoT) is playing a progressive role in the infrastructure of electronics. The increasing amount of IoT devices, however, has led to the emergence of significant privacy and security challenges. Security and Privacy Issues in Sensor Networks and IoT is a collection of innovative research on the methods and applications of protection disputes in the internet of things and other computing structures. While highlighting topics that include cyber defense, digital forensics, and intrusion detection, this book is ideally designed for security analysts, IT specialists, software developers, computer engineers, industry professionals, academicians, students, and researchers seeking current research on defense concerns in cyber physical systems.

In the world of mathematics and computer science, technological advancements are constantly being researched and applied to ongoing issues. Setbacks in social networking, engineering, and automation are themes that affect everyday life, and researchers have been looking for new techniques in which to solve these challenges. Graph theory is a widely studied topic that is now being applied to real-life problems. Advanced Applications of Graph Theory in Modern Society is an essential reference source that discusses recent developments on graph theory, as well as its representation in social networks, artificial neural networks, and many complex networks. The book aims to study results that are useful in the fields of robotics and machine learning and will examine different engineering issues that are closely related to fuzzy graph theory. Featuring research on topics such as artificial neural systems and robotics, this book is ideally designed for mathematicians, research scholars, practitioners, professionals, engineers, and students seeking an innovative overview of graphic theory.

The emergence of cloud computing, internet of things, mobile technologies, and social networking have created better-connected members of the public who are digitally linked with each other in real time. Establishing this two-way interaction between citizens and governments has thus become attractive and an expected feature of governments worldwide. Previously, federal and local governments relied on first-generation technologies to provide basic levels of automation and digitization. Now, because of their desire to become more open, transparent, accountable, and connected, newer technologies including cloud computing, mobile networking, big data analytics, Web 2.0, and social media must be developed and utilized. Web 2.0 and Cloud Technologies for Implementing Connected Government is an essential reference source that presents various dimensions of connected government and connected e-governance visions as well as the latest emerging technologies. Offering development methodologies, practical examples, best practices, case studies, and the latest research, this book covers new strategies for implementing better-connected government models and the technologies that serve to establish these frameworks, including in-depth examinations of mobile technologies, automation, business intelligence, etc. as well as the various ethical and security issues surrounding the use and protection of data. This book is essential for federal, state, and local government officials; policymakers; civil servants; IT specialists; security analysts; academicians; researchers; and students.

Big Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time. In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data. The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting. The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.

This book is a general introduction to the statistical analysis of networks, and can serve both as a research monograph and as a textbook. Numerous fundamental tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, graph-based semi-supervised learning and sampling in networks. The description of these concepts is self-contained, with both theoretical justifications and applications provided for the presented algorithms.Researchers, including postgraduate students, working in the area of network science, complex network analysis, or social network analysis, will find up-to-date statistical methods relevant to their research tasks. This book can also serve as textbook material for courses related to thestatistical approach to the analysis of complex networks.In general, the chapters are fairly independent and self-supporting, and the book could be used for course composition "a la carte". Nevertheless, Chapter 2 is needed to a certain degree for all parts of the book. It is also recommended to read Chapter 4 before reading Chapters 5 and 6, but this is not absolutely necessary. Reading Chapter 3 can also be helpful before reading Chapters 5 and 7. As prerequisites for reading this book, a basic knowledge in probability, linear algebra and elementary notions of graph theory is advised. Appendices describing required notions from the above mentioned disciplines have been added to help readers gain further understanding.

The Dark Web is a known hub that hosts myriad illegal activities behind the veil of anonymity for its users. For years now, law enforcement has been struggling to track these illicit activities and put them to an end. However, the depth and anonymity of the Dark Web has made these efforts difficult, and as cyber criminals have more advanced technologies available to them, the struggle appears to only have the potential to worsen. Law enforcement and government organizations also have emerging technologies on their side, however. It is essential for these organizations to stay up to date on these emerging technologies, such as computational intelligence, in order to put a stop to the illicit activities and behaviors presented in the Dark Web. Using Computational Intelligence for the Dark Web and Illicit Behavior Detection presents the emerging technologies and applications of computational intelligence for the law enforcement of the Dark Web. It features analysis into cybercrime data, examples of the application of computational intelligence in the Dark Web, and provides future opportunities for growth in this field. Covering topics such as cyber threat detection, crime prediction, and keyword extraction, this premier reference source is an essential resource for government organizations, law enforcement agencies, non-profit organizations, politicians, computer scientists, researchers, students, and academicians.

Vehicular networks were first developed to ensure safe driving and to extend the Internet to the road. However, we can now see that the ability of vehicles to engage in cyber-activity may result in tracking and privacy violations through the interception of messages, which are frequently exchanged on road. This book serves as a guide for students, developers and researchers who are interested in vehicular networks and the associated security and privacy issues. It facilitates the understanding of the technologies used and their various types, highlighting the importance of privacy and security issues and the direct impact they have on the safety of their users. It also explains various solutions and proposals to protect location and identity privacy, including two anonymous authentication methods that preserve identity privacy and a total of five schemes that preserve location privacy in the vehicular ad hoc networks and the cloud-enabled internet of vehicles, respectively.

In its 8th edition the SAFE conference continues to publish important research contributing to the development of the field. The papers included in this volume are the work of engineers, scientists, field researchers, managers and other specialists involved in one or more of the theoretical and practical aspects of safety and security. Safety and Security Engineering, due to its special nature, is an interdisciplinary area of research and application that brings together in a systematic way, many disciplines of engineering, from the traditional to the most technologically advanced. This volume covers topics such as crisis management, security engineering, natural and man-made disasters and emergencies, risk management, and control, protection and mitigation issues. Specific themes include: Risk analysis, assessment and management; System safety engineering; Incident monitoring; Information and communication security; Disaster management; Emergency response; Critical infrastructure protection; Counter terrorism issues; Human factors; Transportation safety and security; Modelling and experiments; Security surveillance systems; Loss prevention; BIM in Safety and Security.