Since the advent of the internet, online communities have emerged as a way for users to share their common interests and connect with others with ease. As the possibilities of the online world grew and the COVID-19 pandemic raged across the world, many organizations recognized the utility in not only providing further services online, but also in transitioning operations typically fulfilled in-person to an online space. As society approaches a reality in which most community practices have moved to online spaces, it is essential that community leaders remain knowledgeable on the best practices in cultivating engagement. Community Engagement in the Online Space evaluates key issues and practices pertaining to community engagement in remote settings. It analyzes various community engagement efforts within remote education, online groups, and remote work. This book further reviews the best practices for community engagement and considerations for the optimization of these practices for effective virtual delivery to support emergency environmental challenges, such as pandemic conditions. Covering topics such as community belonging, global health virtual practicum, and social media engagement, this premier reference source is an excellent resource for program directors, faculty and administrators of both K-12 and higher education, students of higher education, business leaders and executives, IT professionals, online community moderators, librarians, researchers, and academicians.

With the growing maturity and stability of digitization and edge technologies, vast numbers of digital entities, connected devices, and microservices interact purposefully to create huge sets of poly-structured digital data. Corporations are continuously seeking fresh ways to use their data to drive business innovations and disruptions to bring in real digital transformation. Data science (DS) is proving to be the one-stop solution for simplifying the process of knowledge discovery and dissemination out of massive amounts of multi-structured data. Supported by query languages, databases, algorithms, platforms, analytics methods and machine and deep learning (ML and DL) algorithms, graphs are now emerging as a new data structure for optimally representing a variety of data and their intimate relationships. Compared to traditional analytics methods, the connectedness of data points in graph analytics facilitates the identification of clusters of related data points based on levels of influence, association, interaction frequency and probability. Graph analytics is being empowered through a host of path-breaking analytics techniques to explore and pinpoint beneficial relationships between different entities such as organizations, people and transactions. This edited book aims to explain the various aspects and importance of graph data science. The authors from both academia and industry cover algorithms, analytics methods, platforms and databases that are intrinsically capable of creating business value by intelligently leveraging connected data. This book will be a valuable reference for ICTs industry and academic researchers, scientists and engineers, and lecturers and advanced students in the fields of data analytics, data science, cloud/fog/edge architecture, internet of things, artificial intelligence/machine and deep learning, and related fields of applications. It will also be of interest to analytics professionals in industry and IT operations teams.

The concept of autonomic computing seeks to reduce the complexity of pervasively ubiquitous system management and maintenance by shifting the responsibility for low-level tasks from humans to the system while allowing humans to concentrate on high-level tasks. This is achieved by building self-managing systems that are generally capable of self-configuring, self-healing, self-optimising, and self-protecting. Trustworthy autonomic computing technologies are being applied in datacentre and cloud management, smart cities and autonomous systems including driverless cars. However, there are still significant challenges to achieving trustworthiness. This book covers challenges and solutions in autonomic computing trustworthiness from methods and techniques to achieve consistent and reliable system self-management. Researchers, developers and users need to be confident that an autonomic self-managing system will remain correct in the face of any possible contexts and environmental inputs. The book is aimed at researchers in autonomic computing, autonomics and trustworthy autonomics. This will be a go-to book for foundational knowledge, proof of concepts and novel trustworthy autonomic techniques and approaches. It will be useful to lecturers and students of autonomic computing, autonomics and multi-agent systems who need an easy-to-use text with sample codes, exercises, use-case demonstrations. This is also an ideal tutorial guide for independent study with simple and well documented diagrams to explain techniques and processes.

Recent years have seen a proliferation of cybersecurity guidance in the form of government regulations and standards with which organizations must comply. As society becomes more heavily dependent on cyberspace, increasing levels of security measures will need to be established and maintained to protect the confidentiality, integrity, and availability of information; the privacy of consumers; and the continuity of economic activity. Compliance is a measure of the extent to which a current state is in conformance with a desired state. The desired state is commonly operationalized through specific business objectives, professional standards, and regulations. Assurance services provide a means of evaluating the level of compliance with various cybersecurity requirements. The proposed book will summarize current cybersecurity guidance and provide a compendium of innovative and state-of-the-art compliance and assurance practices and tools that can function both as a reference and pedagogical source for practitioners and educators. This publication will provide a synopsis of current cybersecurity guidance that organizations should consider in establishing and updating their cybersecurity systems. Assurance services will also be addressed so that management and their auditors can regularly evaluate their extent of compliance. This book should be published because its theme will provide company management, practitioners, and academics with a good summary of current guidance and how to conduct assurance of appropriate compliance.

Opinion Mining and Text Analytics on Literary Works and Social Media introduces the use of artificial intelligence and big data analytics techniques which can apply opinion mining and text analytics on literary works and social media. This book focuses on theories, method and approaches in which data analytic techniques can be used to analyze data from social media, literary books, novels, news, texts, and beyond to provide a meaningful pattern. The subject area of this book is multidisciplinary; related to data science, artificial intelligence, social science and humanities, and literature. This is an essential resource for scholars, Students and lecturers from various fields of data science, artificial intelligence, social science and humanities, and literature, university libraries, new agencies, and many more.

Digital transformation in organizations optimizes the business processes but also brings additional challenges in the form of security threats and vulnerabilities. Cyberattacks incur financial losses for organizations and can affect their reputations. Due to this, cybersecurity has become critical for business enterprises. Extensive technological adoption in businesses and the evolution of FinTech applications require reasonable cybersecurity measures to protect organizations from internal and external security threats. Recent advances in the cybersecurity domain such as zero trust architecture, application of machine learning, and quantum and post-quantum cryptography have colossal potential to secure technological infrastructures. Cybersecurity Issues and Challenges for Business and FinTech Applications discusses theoretical foundations and empirical studies of cybersecurity implications in global digital transformation and considers cybersecurity challenges in diverse business areas. Covering essential topics such as artificial intelligence, social commerce, and data leakage, this reference work is ideal for cybersecurity professionals, business owners, managers, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

Cyber security is a key focus in the modern world as more private information is stored and saved online. In order to ensure vital information is protected from various cyber threats, it is essential to develop a thorough understanding of technologies that can address cyber security challenges. Artificial intelligence has been recognized as an important technology that can be employed successfully in the cyber security sector. Due to this, further study on the potential uses of artificial intelligence is required. The Handbook of Research on Cyber Security Intelligence and Analytics discusses critical artificial intelligence technologies that are utilized in cyber security and considers various cyber security issues and their optimal solutions supported by artificial intelligence. Covering a range of topics such as malware, smart grid, data breachers, and machine learning, this major reference work is ideal for security analysts, cyber security specialists, data analysts, security professionals, computer scientists, government officials, researchers, scholars, academicians, practitioners, instructors, and students.

Developing nations have seen many technological advances in the last decade. Although beneficial and progressive, they can lead to unsafe mobile devices, system networks, and internet of things (IoT) devices, causing security vulnerabilities that can have ripple effects throughout society. While researchers attempt to find solutions, improper implementation and negative uses of technology continue to create new security threats to users. Cybersecurity Capabilities in Developing Nations and Its Impact on Global Security brings together research-based chapters and case studies on systems security techniques and current methods to identify and overcome technological vulnerabilities, emphasizing security issues in developing nations. Focusing on topics such as data privacy and security issues, this book is an essential reference source for researchers, university academics, computing professionals, and upper-level students in developing countries interested in the techniques, laws, and training initiatives currently being implemented and adapted for secure computing.

Cybersecurity is vital for all businesses, regardless of sector. With constant threats and potential online dangers, businesses must remain aware of the current research and information available to them in order to protect themselves and their employees. Maintaining tight cybersecurity can be difficult for businesses as there are so many moving parts to contend with, but remaining vigilant and having protective measures and training in place is essential for a successful company. The Research Anthology on Business Aspects of Cybersecurity considers all emerging aspects of cybersecurity in the business sector including frameworks, models, best practices, and emerging areas of interest. This comprehensive reference source is split into three sections with the first discussing audits and risk assessments that businesses can conduct to ensure the security of their systems. The second section covers training and awareness initiatives for staff that promotes a security culture. The final section discusses software and systems that can be used to secure and manage cybersecurity threats. Covering topics such as audit models, security behavior, and insider threats, it is ideal for businesses, business professionals, managers, security analysts, IT specialists, executives, academicians, researchers, computer engineers, graduate students, and practitioners.

The artificial intelligence subset machine learning has become a popular technique in professional fields as many are finding new ways to apply this trending technology into their everyday practices. Two fields that have majorly benefited from this are pattern recognition and information security. The ability of these intelligent algorithms to learn complex patterns from data and attain new performance techniques has created a wide variety of uses and applications within the data security industry. There is a need for research on the specific uses machine learning methods have within these fields, along with future perspectives. Machine Learning Techniques for Pattern Recognition and Information Security is a collection of innovative research on the current impact of machine learning methods within data security as well as its various applications and newfound challenges. While highlighting topics including anomaly detection systems, biometrics, and intrusion management, this book is ideally designed for industrial experts, researchers, IT professionals, network developers, policymakers, computer scientists, educators, and students seeking current research on implementing machine learning tactics to enhance the performance of information security.

Every day approximately three-hundred thousand to four-hundred thousand new malware are registered, many of them being adware and variants of previously known malware. Anti-virus companies and researchers cannot deal with such a deluge of malware - to analyze and build patches. The only way to scale the efforts is to build algorithms to enable machines to analyze malware and classify and cluster them to such a level of granularity that it will enable humans (or machines) to gain critical insights about them and build solutions that are specific enough to detect and thwart existing malware and generic-enough to thwart future variants. Advances in Malware and Data-Driven Network Security comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning, and AI as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats. Providing information on advances in malware and data-driven network security as well as future research directions, it is ideal for graduate students, academicians, faculty members, scientists, software developers, security analysts, computer engineers, programmers, IT specialists, and researchers who are seeking to learn and carry out research in the area of malware and data-driven network security.

In recent years, falsification and digital modification of video clips, images, as well as textual contents have become widespread and numerous, especially when deepfake technologies are adopted in many sources. Due to adopted deepfake techniques, a lot of content currently cannot be recognized from its original sources. As a result, the field of study previously devoted to general multimedia forensics has been revived. The Handbook of Research on Advanced Practical Approaches to Deepfake Detection and Applications discusses the recent techniques and applications of illustration, generation, and detection of deepfake content in multimedia. It introduces the techniques and gives an overview of deepfake applications, types of deepfakes, the algorithms and applications used in deepfakes, recent challenges and problems, and practical applications to identify, generate, and detect deepfakes. Covering topics such as anomaly detection, intrusion detection, and security enhancement, this major reference work is a comprehensive resource for cyber security specialists, government officials, law enforcement, business leaders, students and faculty of higher education, librarians, researchers, and academicians.

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.

During the COVID-19 era, the functions of social policy and public administration have undergone a meaningful change, especially with the advancement of digital elements and online and virtual functions. Cyber developments, cyber threats, and the effects of cyberwar on the public administrations of countries have become critical research subjects, and it is important to have resources that can introduce and guide users through the current best practices, laboratory methods, policies, protocols, and more within cyber public administration and social policy. The Handbook of Research on Cyber Approaches to Public Administration and Social Policy focuses on the post-pandemic changes in the functions of social policy and public administration. It also examines the implications of the cyber cosmos on public and social policies and practices from a broad perspective. Covering topics such as intersectional racism, cloud computing applications, and public policies, this major reference work is an essential resource for scientists, laboratory technicians, professionals, technologists, computer scientists, policymakers, students, educators, researchers, and academicians.