Society is continually transforming into a digitally powered reality due to the increased dependence of computing technologies. The landscape of cyber threats is constantly evolving because of this, as hackers are finding improved methods of accessing essential data. Analyzing the historical evolution of cyberattacks can assist practitioners in predicting what future threats could be on the horizon. Real-Time and Retrospective Analyses of Cyber Security is a pivotal reference source that provides vital research on studying the development of cybersecurity practices through historical and sociological analyses. While highlighting topics such as zero trust networks, geopolitical analysis, and cyber warfare, this publication explores the evolution of cyber threats, as well as improving security methods and their socio-technological impact. This book is ideally designed for researchers, policymakers, strategists, officials, developers, educators, sociologists, and students seeking current research on the evolution of cybersecurity methods through historical analysis and future trends.

Heavily dominated by the sector of information and communication technologies, economic organizations pursue digital transformation as a differentiating factor and source of competitive advantage. Understanding the challenges of digital transformation is critical to managers to ensure business sustainability. However, there are some problems, such as architecture, security, and reliability, among others, that bring with them the need for studies and investments in this area to avoid significant financial losses. Digital transformation encompasses and challenges many areas, such as business models, organizational structures, human privacy, management, and more, creating a need to investigate the challenges associated with it to create a roadmap for this new digital transformation era. The Handbook of Research on Digital Transformation and Challenges to Data Security and Privacy presents the main challenges of digital transformation and the threats it poses to information security and privacy, as well as models that can contribute to solving these challenges in economic organizations. While highlighting topics such as information systems, digital trends, and information governance, this book is ideally intended for managers, data analysts, cybersecurity professionals, IT specialists, practitioners, researchers, academicians, and students working in fields that include digital transformation, information management, information security, information system reliability, business continuity, and data protection.

The COVID-19 pandemic caused the largest systemic disruption in history. The pandemic was a complex phenomenon that impacted economic, political, and education systems. The pandemic had widespread business impacts, having forced many businesses to close, and the world is still impacted by the effects of supply chain disruptions. The pandemic also impacted political systems with disputes over mask mandates, lockdowns, and vaccine distribution. The COVID-19 pandemic further caused the most extensive education system disruption in history. The pandemic has highlighted the world’s complex interdependent structures, and it will require a multidisciplinary systems thinking approach for post-pandemic recovery and future pandemic prevention. Reimagining Systems Thinking in a Post-Pandemic World examines the role of systems thinking in a post-pandemic world. It identifies effective models of systems thinking and destems design and generates continuous knowledge building on systems thinking by addressing a multitude of industries and service communities. This book provides value in understanding the complexities of an interconnected world and in the exploration of effective approaches to systems thinking and design. Covering topics such as blended learning, local governments, and systems thinking, this premier reference source is an excellent resource for practitioners, policymakers, healthcare providers, business leaders and managers, educators of both K-12 and higher education, pre-service teachers, administrators and faculty, teacher educators, sociologists, librarians, researchers, and academicians.

​This book focuses on a wide range of breakthroughs related to digital biometrics and forensics. The authors introduce the concepts, techniques, methods, approaches and trends needed by cybersecurity specialists and educators for keeping current their biometrics and forensics knowledge. Furthermore, the book provides a glimpse of future directions where biometrics and forensics techniques, policies, applications, and theories are headed. Topics include multimodal biometrics, soft biometrics, mobile biometrics, vehicle biometrics, vehicle forensics, integrity verification of digital content, people identification, biometric-based cybercrime investigation, among others. The book is a rich collection of carefully selected and reviewed manuscripts written by diverse digital biometrics and forensics experts in the listed fields and edited by prominent biometrics and forensics researchers and specialists.

The digital era has redefined our understanding of ethics as a multi-disciplinary phenomenon. The newness of the internet means it is still highly unregulated, which allows for rampant problems encountered by countless internet users. In order to establish a framework to protect digital citizenship, an academic understanding of online ethics is required. Multidisciplinary Approaches to Ethics in the Digital Era examines the concept of ethics in the digital environment through the framework of digitalization. Covering a broad range of topics including ethics in art, organizational ethics, and civil engineering ethics, this book is ideally designed for media professionals, sociologists, programmers, policymakers, government officials, academicians, researchers, and students.

The concept of quantum computing is based on two fundamental principles of quantum mechanics: superposition and entanglement. Instead of using bits, qubits are used in quantum computing, which is a key indicator in the high level of safety and security this type of cryptography ensures. If interfered with or eavesdropped in, qubits will delete or refuse to send, which keeps the information safe. This is vital in the current era where sensitive and important personal information can be digitally shared online. In computer networks, a large amount of data is transferred worldwide daily, including anything from military plans to a country’s sensitive information, and data breaches can be disastrous. This is where quantum cryptography comes into play. By not being dependent on computational power, it can easily replace classical cryptography. Limitations and Future Applications of Quantum Cryptography is a critical reference that provides knowledge on the basics of IoT infrastructure using quantum cryptography, the differences between classical and quantum cryptography, and the future aspects and developments in this field. The chapters cover themes that span from the usage of quantum cryptography in healthcare, to forensics, and more. While highlighting topics such as 5G networks, image processing, algorithms, and quantum machine learning, this book is ideally intended for security professionals, IoT developers, computer scientists, practitioners, researchers, academicians, and students interested in the most recent research on quantum computing.

While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques. This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals. Key Features: This book contains examples and illustrations to demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security. It showcases important security aspects and current trends in the field. It provides an insight of the future research directions in the field. Contents of this book help to prepare the students for exercising better defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation using machine learning based approaches in better manner.