Predictive analytics and data mining technologies and techniques are increasingly being used by the intelligence, counterterrorism, national security and law enforcement communities as part of standard operating procedures. Used appropriately by trained analysts, this technology can do much to unearth patterns in fraud and financial computer crimes, as well as with regard to suspicious activities. Written by a known expert, this work helps agencies become familiar with advanced data mining and provides trainees with a manual covering the use of data mining in operational and investigative procedures. Building on concepts from the author’s previously published work, this volume offers a look into specific predictive analytic techniques, fully explaining how the techniques can be used to combat criminal activity and terrorism. To reinforce the training, it discusses detailed cases in which analysis was used to research and uncover criminal activity and events. The author examines many real-world issues including the debate over security concerns versus legal and privacy issues.

Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, 2nd Edition, describes clearly and simply how crime clusters and other intelligence can be used to deploy security resources most effectively. Rather than being reactive, security agencies can anticipate and prevent crime through the appropriate application of data mining and the use of standard computer programs. Data Mining and Predictive Analysis offers a clear, practical starting point for professionals who need to use data mining in homeland security, security analysis, and operational law enforcement settings. This revised text highlights new and emerging technology, discusses the importance of analytic context for ensuring successful implementation of advanced analytics in the operational setting, and covers new analytic service delivery models that increase ease of use and access to high-end technology and analytic capabilities. The use of predictive analytics in intelligence and security analysis enables the development of meaningful, information based tactics, strategy, and policy decisions in the operational public safety and security environment. Discusses new and emerging technologies and techniques, including up-to-date information on predictive policing, a key capability in law enforcement and security Demonstrates the importance of analytic context beyond software Covers new models for effective delivery of advanced analytics to the operational environment, which have increased access to even the most powerful capabilities Includes terminology, concepts, practical application of these concepts, and examples to highlight specific techniques and approaches in crime and intelligence analysis

Intelligent data analytics for terror threat prediction is an emerging field of research at the intersection of information science and computer science, bringing with it a new era of tremendous opportunities and challenges due to plenty of easily available criminal data for further analysis. This book provides innovative insights that will help obtain interventions to undertake emerging dynamic scenarios of criminal activities. Furthermore, it presents emerging issues, challenges and management strategies in public safety and crime control development across various domains. The book will play a vital role in improvising human life to a great extent. Researchers and practitioners working in the fields of data mining, machine learning and artificial intelligence will greatly benefit from this book, which will be a good addition to the state-of-the-art approaches collected for intelligent data analytics. It will also be very beneficial for those who are new to the field and need to quickly become acquainted with the best performing methods. With this book they will be able to compare different approaches and carry forward their research in the most important areas of this field, which has a direct impact on the betterment of human life by maintaining the security of our society. No other book is currently on the market which provides such a good collection of state-of-the-art methods for intelligent data analytics-based models for terror threat prediction, as intelligent data analytics is a newly emerging field and research in data mining and machine learning is still in the early stage of development.

Mathematical sciences play a key role in many important areas of Homeland Security including data mining and image analysis and voice recognition for intelligence analysis, encryption and decryption for intelligence gathering and computer security, detection and epidemiology of bioterriost attacks to determine their scope, and data fusion to analyze information coming from simultaneously from several sources. This report presents the results of a workshop focusing on mathematical methods and techniques for addressing these areas. The goal of the workshop is to help mathematical scientists and policy makers understand the connections between mathematical sciences research and these homeland security applications.

This book is nothing less than a complete and comprehensive survey of the state-of-the-art of terrorism informatics. It covers the application of advanced methodologies and information fusion and analysis. It also lays out techniques to acquire, integrate, process, analyze, and manage the diversity of terrorism-related information for international and homeland security-related applications. The book details three major areas of terrorism research: prevention, detection, and established governmental responses to terrorism. It systematically examines the current and ongoing research, including recent case studies and application of terrorism informatics techniques. The coverage then presents the critical and relevant social/technical areas to terrorism research including social, privacy, data confidentiality, and legal challenges.

Use Predictive Analytics to Uncover Hidden Patterns and Correlations and Improve Decision-Making Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. In this thoroughly updated guide, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students. Delen's holistic approach covers key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. Balancing theory and practice, Delen presents intuitive conceptual illustrations, realistic example problems, and real-world case studies—including lessons from failed projects. It's all designed to help you gain a practical understanding you can apply for profit. * Leverage knowledge extracted via data mining to make smarter decisions * Use standardized processes and workflows to make more trustworthy predictions * Predict discrete outcomes (via classification), numeric values (via regression), and changes over time (via time-series forecasting) * Understand predictive algorithms drawn from traditional statistics and advanced machine learning * Discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection

Presents various challenges faced by security policy makers and risk analysts, and mathematical approaches that inform homeland security policy development and decision support Compiled by a group of highly qualified editors, this book provides a clear connection between risk science and homeland security policy making and includes top-notch contributions that uniquely highlight the role of risk analysis for informing homeland security policy decisions. Featuring discussions on various challenges faced in homeland security risk analysis, the book seamlessly divides the subject of risk analysis for homeland security into manageable chapters, which are organized by the concept of risk-informed decisions, methodology for applying risk analysis, and relevant examples and case studies. Applied Risk Analysis for Guiding Homeland Security Policy and Decisions offers an enlightening overview of risk analysis methods for homeland security. For instance, it presents readers with an exploration of radiological and nuclear risk assessment, along with analysis of uncertainties in radiological and nuclear pathways. It covers the advances in risk analysis for border security, as well as for cyber security. Other topics covered include: strengthening points of entry; systems modeling for rapid containment and casualty mitigation; and disaster preparedness and critical infrastructure resilience. Highlights how risk analysis helps in the decision-making process for homeland security policy Presents specific examples that detail how various risk analysis methods provide decision support for homeland security policy makers and risk analysts Describes numerous case studies from academic, government, and industrial perspectives that apply risk analysis methods for addressing challenges within the U.S. Department of Homeland Security (DHS) Offers detailed information regarding each of the five DHS missions: prevent terrorism and enhance security; secure and manage our borders; enforce and administer our immigration laws; safeguard and secure cyberspace; and strengthen national preparedness and resilience Discusses the various approaches and challenges faced in homeland risk analysis and identifies improvements and methodological advances that influenced DHS to adopt an increasingly risk-informed basis for decision-making Written by top educators and professionals who clearly illustrate the link between risk science and homeland security policy making Applied Risk Analysis for Guiding Homeland Security Policy and Decisions is an excellent textbook and/or supplement for upper-undergraduate and graduate-level courses related to homeland security risk analysis. It will also be an extremely beneficial resource and reference for homeland security policy analysts, risk analysts, and policymakers from private and public sectors, as well as researchers, academics, and practitioners who utilize security risk analysis methods.