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.

Analyzing and Securing Social Networks focuses on the two major technologies that have been developed for online social networks (OSNs): (i) data mining technologies for analyzing these networks and extracting useful information such as location, demographics, and sentiments of the participants of the network, and (ii) security and privacy technologies that ensure the privacy of the participants of the network as well as provide controlled access to the information posted and exchanged by the participants. The authors explore security and privacy issues for social media systems, analyze such systems, and discuss prototypes they have developed for social media systems whose data are represented using semantic web technologies. These experimental systems have been developed at The University of Texas at Dallas. The material in this book, together with the numerous references listed in each chapter, have been used for a graduate-level course at The University of Texas at Dallas on analyzing and securing social media. Several experimental systems developed by graduate students are also provided. The book is divided into nine main sections: (1) supporting technologies, (2) basics of analyzing and securing social networks, (3) the authors' design and implementation of various social network analytics tools, (4) privacy aspects of social networks, (5) access control and inference control for social networks, (6) experimental systems designed or developed by the authors on analyzing and securing social networks, (7) social media application systems developed by the authors, (8) secure social media systems developed by the authors, and (9) some of the authors' exploratory work and further directions.

Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges. Three parts divide Data Mining: Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision support, statistics, parallel processing, and architectural support for data mining Part II presents tools and techniques - getting the data ready, carrying out the mining, pruning the results, evaluating outcomes, defining specific approaches, examining a specific technique based on logic programming, and citing literature and vendors for up-to-date information Part III examines emerging trends - mining distributed and heterogeneous data sources; multimedia data, such as text, images, video; mining data on the World Wide Web; metadata aspects of mining; and privacy issues. This self-contained book also contains two appendices providing exceptional information on technologies, such as data management, and artificial intelligence. Is there a need for mining? Do you have the right tools? Do you have the people to do the work? Do you have sufficient funds allocated to the project? All these answers must be answered before embarking on a project. Data Mining provides singular guidance on appropriate applications for specific techniques as well as thoroughly assesses valuable product information.

Although the use of data mining for security and malware detection is quickly on the rise, most books on the subject provide high-level theoretical discussions to the near exclusion of the practical aspects. Breaking the mold, Data Mining Tools for Malware Detection provides a step-by-step breakdown of how to develop data mining tools for malware detection. Integrating theory with practical techniques and experimental results, it focuses on malware detection applications for email worms, malicious code, remote exploits, and botnets.

The authors describe the systems they have designed and developed: email worm detection using data mining, a scalable multi-level feature extraction technique to detect malicious executables, detecting remote exploits using data mining, and flow-based identification of botnet traffic by mining multiple log files. For each of these tools, they detail the system architecture, algorithms, performance results, and limitations.

• Discusses data mining for emerging applications, including adaptable malware detection, insider threat detection, firewall policy analysis, and real-time data mining
• Includes four appendices that provide a firm foundation in data management, secure systems, and the semantic web
• Describes the authors tools for stream data mining

From algorithms to experimental results, this is one of the few books that will be equally valuable to those in industry, government, and academia. It will help technologists decide which tools to select for specific applications, managers will learn how to determine whether or not to proceed with a data mining project, and developers will find innovative alternative designs for a range of applications.

With an ever-increasing amount of information on the web, it is critical to understand the pedigree, quality, and accuracy of your data. Using provenance, you can ascertain the quality of data based on its ancestral data and derivations, track back to sources of errors, allow automatic re-enactment of derivations to update data, and provide attribution of the data source. Secure Data Provenance and Inference Control with Semantic Web supplies step-by-step instructions on how to secure the provenance of your data to make sure it is safe from inference attacks. It details the design and implementation of a policy engine for provenance of data and presents case studies that illustrate solutions in a typical distributed health care system for hospitals. Although the case studies describe solutions in the health care domain, you can easily apply the methods presented in the book to a range of other domains. The book describes the design and implementation of a policy engine for provenance and demonstrates the use of Semantic Web technologies and cloud computing technologies to enhance the scalability of solutions. It covers Semantic Web technologies for the representation and reasoning of the provenance of the data and provides a unifying framework for securing provenance that can help to address the various criteria of your information systems. Illustrating key concepts and practical techniques, the book considers cloud computing technologies that can enhance the scalability of solutions. After reading this book you will be better prepared to keep up with the on-going development of the prototypes, products, tools, and standards for secure data management, secure Semantic Web, secure web services, and secure cloud computing.

Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges.

Three parts divide Data Mining:
o Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision support, statistics, parallel processing, and architectural support for data mining
o Part II presents tools and techniques - getting the data ready, carrying out the mining, pruning the results, evaluating outcomes, defining specific approaches, examining a specific technique based on logic programming, and citing literature and vendors for up-to-date information
o Part III examines emerging trends - mining distributed and heterogeneous data sources; multimedia data, such as text, images, video; mining data on the World Wide Web; metadata aspects of mining; and privacy issues.

This self-contained book also contains two appendices providing exceptional information on technologies, such as data management, and artificial intelligence.

Is there a need for mining? Do you have the right tools? Do you have the people to do the work? Do you have sufficient funds allocated to the project? All these answers must be answered before embarking on a project. Data Mining provides singular guidance on appropriate applications for specific techniques as well as thoroughly assesses valuable product information.

With an ever-increasing amount of information on the web, it is critical to understand the pedigree, quality, and accuracy of your data. Using provenance, you can ascertain the quality of data based on its ancestral data and derivations, track back to sources of errors, allow automatic re-enactment of derivations to update data, and provide attribution of the data source. Secure Data Provenance and Inference Control with Semantic Web supplies step-by-step instructions on how to secure the provenance of your data to make sure it is safe from inference attacks. It details the design and implementation of a policy engine for provenance of data and presents case studies that illustrate solutions in a typical distributed health care system for hospitals. Although the case studies describe solutions in the health care domain, you can easily apply the methods presented in the book to a range of other domains. The book describes the design and implementation of a policy engine for provenance and demonstrates the use of Semantic Web technologies and cloud computing technologies to enhance the scalability of solutions. It covers Semantic Web technologies for the representation and reasoning of the provenance of the data and provides a unifying framework for securing provenance that can help to address the various criteria of your information systems. Illustrating key concepts and practical techniques, the book considers cloud computing technologies that can enhance the scalability of solutions. After reading this book you will be better prepared to keep up with the on-going development of the prototypes, products, tools, and standards for secure data management, secure Semantic Web, secure web services, and secure cloud computing.

Semantic Webs promise to revolutionize the way computers find and integrate data over the internet. They will allow Web agents to share and reuse data across applications, enterprises, and community boundaries. However, this improved accessibility poses a greater threat of unauthorized access, which could lead to the malicious corruption of information. Building Trustworthy Semantic Webs addresses the urgent demand for the development of effective mechanisms that will protect and secure semantic Webs.
Design Flexible Security Policies to Improve Efficiency
Securing semantic Webs involves the formation of policies that will dictate what type of access Web agents are allowed. This text provides the tools needed to engineer these policies and secure individual components of the semantic Web, such as XML, RDF, and OWL. It also examines how to control unauthorized inferences on the semantic Web. Since this technology is not fully realized, the book emphasizes the importance of integrating security features into semantic Webs at the onset of their development.
Through its expansive coverage, Building Trustworthy Semantic Webs describes how the creation of semantic security standards will ensure the dependability of semantic Webs. It provides Web developers with the tools they need to protect sensitive information and guarantee the success of semantic Web applications.

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, each section focusing on some key concept of secure databases and applications. Database and Applications Security provides a comprehensive overview and focuses on all aspects of the technology including secure relational databases, inference problems, secure object databases, secure distributed databases and emerging applications.

This is the first joint working conference between the IFIP Working Groups 11. 1 and 11. 5. We hope this joint conference will promote collaboration among researchers who focus on the security management issues and those who are interested in integrity and control of information systems. Indeed, as management at any level may be increasingly held answerable for the reliable and secure operation of the information systems and services in their respective organizations in the same manner as they are for financial aspects of the enterprise, there is an increasing need for ensuring proper standards of integrity and control in information systems in order to ensure that data, software and, ultimately, the business processes are complete, adequate and valid for intended functionality and expectations of the owner (i. e. the user organization). As organizers, we would like to thank the members of the international program committee for their review work during the paper selection process. We would also like to thank the authors of the invited papers, who added valuable contribution to this first joint working conference. Paul Dowland X. Sean Wang December 2005 Contents Preface vii Session 1 - Security Standards Information Security Standards: Adoption Drivers (Invited Paper) 1 JEAN-NOEL EZINGEARD AND DAVID BIRCHALL Data Quality Dimensions for Information Systems Security: A Theorectical Exposition (Invited Paper) 21 GURVIRENDER TEJAY, GURPREET DHILLON, AND AMITA GOYAL CHIN From XML to RDF: Syntax, Semantics, Security, and Integrity (Invited Paper) 41 C. FARKAS, V. GowADiA, A. JAIN, AND D.

Efficient access to data, sharing data, extracting information from data, and making use of the information have become urgent needs for today's corporations. With so much data on the Web, managing it with conventional tools is becoming almost impossible. New tools and techniques are necessary to provide interoperability as well as warehousing between multiple data sources and systems, and to extract information from the databases. XML Databases and the Semantic Web focuses on critical and new Web technologies needed for organizations to carry out transactions on the Web, to understand how to use the Web effectively, and to exchange complex documents on the Web. This reference for database administrators, database designers, and Web designers working in tandem with database technologists covers three emerging technologies of significant impact for electronic business: Extensible Markup Language (XML), semi-structured databases, and the semantic Web. The first two parts of the book explore these emerging technologies. The third part highlights the implications of these technologies for e-business. Overall, the book presents a comprehensive view of critical technologies for the Web in general and XML in particular. The semantic Web, XML, and semi-structured databases are still relatively new technologies that integrate many other technologies. As these technologies and integration of these advances mature, we can expect to see progress in the semantic web. The information contained in XML Databases and the Semantic Web is essential to the future success of effective e-business on the Web.

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.

Semantic Webs promise to revolutionize the way computers find and integrate data over the internet. They will allow Web agents to share and reuse data across applications, enterprises, and community boundaries. However, this improved accessibility poses a greater threat of unauthorized access, which could lead to the malicious corruption of information. Building Trustworthy Semantic Webs addresses the urgent demand for the development of effective mechanisms that will protect and secure semantic Webs. Design Flexible Security Policies to Improve Efficiency Securing semantic Webs involves the formation of policies that will dictate what type of access Web agents are allowed. This text provides the tools needed to engineer these policies and secure individual components of the semantic Web, such as XML, RDF, and OWL. It also examines how to control unauthorized inferences on the semantic Web. Since this technology is not fully realized, the book emphasizes the importance of integrating security features into semantic Webs at the onset of their development. Through its expansive coverage, Building Trustworthy Semantic Webs describes how the creation of semantic security standards will ensure the dependability of semantic Webs. It provides Web developers with the tools they need to protect sensitive information and guarantee the success of semantic Web applications.

Analyzing and Securing Social Networks focuses on the two major technologies that have been developed for online social networks (OSNs): (i) data mining technologies for analyzing these networks and extracting useful information such as location, demographics, and sentiments of the participants of the network, and (ii) security and privacy technologies that ensure the privacy of the participants of the network as well as provide controlled access to the information posted and exchanged by the participants. The authors explore security and privacy issues for social media systems, analyze such systems, and discuss prototypes they have developed for social media systems whose data are represented using semantic web technologies. These experimental systems have been developed at The University of Texas at Dallas. The material in this book, together with the numerous references listed in each chapter, have been used for a graduate-level course at The University of Texas at Dallas on analyzing and securing social media. Several experimental systems developed by graduate students are also provided. The book is divided into nine main sections: (1) supporting technologies, (2) basics of analyzing and securing social networks, (3) the authors' design and implementation of various social network analytics tools, (4) privacy aspects of social networks, (5) access control and inference control for social networks, (6) experimental systems designed or developed by the authors on analyzing and securing social networks, (7) social media application systems developed by the authors, (8) secure social media systems developed by the authors, and (9) some of the authors' exploratory work and further directions.

The explosion of Web-based data has created a demand among executives and technologists for methods to identify, gather, analyze, and utilize data that may be of value to corporations and organizations. The emergence of data mining, and the larger field of Web mining, has businesses lost within a confusing maze of mechanisms and strategies for obtaining and managing crucial intelligence.

Web Data Mining and Applications in Business Intelligence and Counter-Terrorism responds by presenting a clear and comprehensive overview of Web mining, with emphasis on CRM and, for the first time, security and counter-terrorism applications. The tools and methods of Web mining are revealed in an easy-to-understand style, emphasizing the importance of practical, hands-on experience in the creation of successful e-business solutions.

The author, a program director for Data and Applications Security at the National Science Foundations, details how both opportunities and dangers on the Web can be identified and managed. Armed with the knowledge contained in this book, businesses can collect and analyze Web-based data to help develop customer relationships, increase sales, and identify existing and potential threats. Organizations can apply these same Web mining techniques to battle the real and present danger of terrorism, demonstrating Web mining's critical role in the intelligence arsenal.