This Festschrift is in honor of Sushil Jajodia, Professor in the George Mason University, USA, on the occasion of his 70th birthday. This book contains papers written in honor of Sushil Jajodia, of his vision and his achievements. Sushil has sustained a highly active research agenda spanning several important areas in computer security and privacy, and established himself as a leader in the security research community through unique scholarship and service. He has extraordinarily impacted the scientific and academic community, opening and pioneering new directions of research, and significantly influencing the research and development of security solutions worldwide. Also, his excellent record of research funding shows his commitment to sponsored research and the practical impact of his work. The research areas presented in this Festschrift include membrane computing, spiking neural networks, phylogenetic networks, ant colonies optimization, work bench for bio-computing, reaction systems, entropy of computation, rewriting systems, and insertion-deletion systems.

This book is about database security and auditing. You will learn many methods and techniques that will be helpful in securing, monitoring and auditing database environments. It covers diverse topics that include all aspects of database security and auditing - including network security for databases, authentication and authorization issues, links and replication, database Trojans, etc. You will also learn of vulnerabilities and attacks that exist within various database environments or that have been used to attack databases (and that have since been fixed). These will often be explained to an “internals level. There are many sections which outline the “anatomy of an attack – before delving into the details of how to combat such an attack. Equally important, you will learn about the database auditing landscape – both from a business and regulatory requirements perspective as well as from a technical implementation perspective. * Useful to the database administrator and/or security administrator - regardless of the precise database vendor (or vendors) that you are using within your organization. * Has a large number of examples - examples that pertain to Oracle, SQL Server, DB2, Sybase and even MySQL.. * Many of the techniques you will see in this book will never be described in a manual or a book that is devoted to a certain database product. * Addressing complex issues must take into account more than just the database and focusing on capabilities that are provided only by the database vendor is not always enough. This book offers a broader view of the database environment - which is not dependent on the database platform - a view that is important to ensure good database security.

Handbook of Database Security: Applications and Trends provides an up-to-date overview of data security models, techniques, and architectures in a variety of data management applications and settings. In addition to providing an overview of data security in different application settings, this book includes an outline for future research directions within the field. The book is designed for industry practitioners and researchers, and is also suitable for advanced-level students in computer science.

This is the first book to provide an in-depth coverage of all the developments, issues and challenges in secure databases and applications. It provides directions for data and application security, including securing emerging applications such as bioinformatics, stream information processing and peer-to-peer computing. Divided into eight sections,

Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.

The application of data warehousing and data mining techniques to computer security is an important emerging area, as information processing and internet accessibility costs decline and more and more organizations become vulnerable to cyber attacks. These security breaches include attacks on single computers, computer networks, wireless networks, databases, or authentication compromises. This book describes data warehousing and data mining techniques that can be used to detect attacks. It is designed to be a useful handbook for practitioners and researchers in industry, and is also suitable as a text for advanced-level students in computer science.

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.