Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.

Today's malware mutates randomly to avoid detection, but reactively adaptive malware is more intelligent, learning and adapting to new computer defenses on the fly. Using the same algorithms that antivirus software uses to detect viruses, reactively adaptive malware deploys those algorithms to outwit antivirus defenses and to go undetected. This book provides details of the tools, the types of malware the tools will detect, implementation of the tools in a cloud computing framework and the applications for insider threat detection.

Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science.

The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detection, E-Learning, Online Examinations, Cybersecurity noisy data removal, Secure Smart Power Systems, Security Visualization and Monitoring. Researchers and professionals alike will find the chapters an essential read for further research on the topic.

In recent years, a considerable amount of effort has been devoted to cyber-threat protection of computer systems which is one of the most critical cybersecurity tasks for single users and businesses since even a single attack can result in compromised data and sufficient losses. Massive losses and frequent attacks dictate the need for accurate and timely detection methods. Current static and dynamic methods do not provide efficient detection, especially when dealing with zero-day attacks. For this reason, big data analytics and machine intelligencebased techniques can be used. This book brings together researchers in the field of big data analytics and intelligent systems for cyber threat intelligence CTI and key data to advance the mission of anticipating, prohibiting, preventing, preparing, and responding to internal security. The wide variety of topics it presents offers readers multiple perspectives on various disciplines related to big data analytics and intelligent systems for cyber threat intelligence applications. Technical topics discussed in the book include: • Big data analytics for cyber threat intelligence and detection • Artificial intelligence analytics techniques • Real-time situational awareness • Machine learning techniques for CTI • Deep learning techniques for CTI • Malware detection and prevention techniques • Intrusion and cybersecurity threat detection and analysis • Blockchain and machine learning techniques for CTI

Perspectives on Artificial Intelligence in Times of Turbulence: Theoretical Background to Applications offers a comprehensive exploration of the intricate relationship between artificial intelligence (AI) and the ever-changing landscape of our society. The book defines AI as machines capable of performing tasks that were once exclusive to human cognition. However, it emphasizes the current limitations of AI, dispelling the notion of sophisticated cyborgs depicted in popular culture. These machines lack self-awareness, struggle with understanding context—especially in language—and are constrained by historical data and predefined parameters. This distinction sets the stage for examining AI's impact on the job market and the evolving roles of humans and machines. Rather than portraying AI as a threat, this book highlights the symbiotic relationship between humans and machines. It recognizes that while certain jobs may become obsolete, new opportunities will emerge. The unique abilities of human beings—such as relational skills, emotional intelligence, adaptability, and understanding of differences—will continue to be indispensable in a rapidly transforming society. Its perspectives cover a wide range of topics such as business sustainability, change management, cybersecurity, digital economy and transformation, information systems management, management models and tools, and continuous improvement are comprehensively addressed. Additionally, the book delves into healthcare, telemedicine, Health 4.0, privacy and security, knowledge management, learning, and presents real-world case studies. Designed for researchers and professionals seeking to enhance their knowledge and research capabilities, this book offers a consistent theoretical and practical foundation. It serves as a springboard for further studies, supports change management initiatives within organizations, and facilitates knowledge sharing among experts. This book is an essential companion for colleges with master's and Ph.D. degree investigators, and researchers across a wide range of disciplines.

This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.