Covering research at the frontier of this field, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques presents state-of-the-art privacy-preserving data mining techniques for application domains, such as medicine and social networks, that face the increasing heterogeneity and complexity of new forms of data. Renowned authorities from prominent organizations not only cover well-established results-they also explore complex domains where privacy issues are generally clear and well defined, but the solutions are still preliminary and in continuous development. Divided into seven parts, the book provides in-depth coverage of the most novel reference scenarios for privacy-preserving techniques. The first part gives general techniques that can be applied to various applications discussed in the rest of the book. The second section focuses on the sanitization of network traces and privacy in data stream mining. After the third part on privacy in spatio-temporal data mining and mobility data analysis, the book examines time series analysis in the fourth section, explaining how a perturbation method and a segment-based method can tackle privacy issues of time series data. The fifth section on biomedical data addresses genomic data as well as the problem of privacy-aware information sharing of health data. In the sixth section on web applications, the book deals with query log mining and web recommender systems. The final part on social networks analyzes privacy issues related to the management of social network data under different perspectives. While several new results have recently occurred in the privacy, database, and data mining research communities, a uniform presentation of up-to-date techniques and applications is lacking. Filling this void, Privacy-Aware Knowledge Discovery presents novel algorithms, patterns, and models, along with a significant collection of open problems for future investigation.

Most widely available approaches to semantic integration provide ad-hoc, non-systematic, subjective manual mappings that lead to procrustean amalgamations to fit the target standard, an outcome that pleases no one. Written by experts in the field, Theories of Geographic Concepts: Ontological Approaches to Semantic Integration emphasizes the real issues involved in integrating existing geo-ontologies. The book addresses theoretical, formal, and pragmatic issues of geographic knowledge representation and integration based on an ontological approach. The authors highlight the importance of philosophical, cognitive, and formal theories in preserving the semantics of geographic concepts during ontology development and integration. They elucidate major theoretical issues, then introduce a number of formal tools. The book delineates a general framework with the necessary processes and guidelines to ontology integration and applies it to a selection of ontology integration cases. It concludes with a retrospection of key issues and identifies open research questions. Copiously illustrated, the book contains more than 80 illustrations and several examples to various approaches that provide a better understanding of the complexity of ontology integration tasks. The authors provide guidance on selecting the most appropriate approach and details on its application to indicative integration problems.

Although much literature exists on the subject of RSA and public-key cryptography, until now there has been no single source that reveals recent developments in the area at an accessible level. Acclaimed author Richard A. Mollin brings together all of the relevant information available on public-key cryptography (PKC), from RSA to the latest applications of PKC, including electronic cash, secret broadcasting, secret balloting systems, various banking and payment protocols, high security logins, smart cards, and biometrics. Moreover, he covers public-key infrastructure (PKI) and its various security applications. Throughout the book, Mollin gives a human face to cryptography by including nearly 40 biographies of the individuals who helped develop cryptographic concepts. He includes a number of illustrative and motivating examples, as well as optional topics that go beyond the basics, such as Lenstra's elliptic curve method and the number field sieve. From history and basic concepts to future trends and emerging applications, this book provides a rigorous and detailed treatment of public-key cryptography. Accessible to anyone from the senior undergraduate to the research scientist, RSA and Public-Key Cryptography offers challenging and inspirational material for all readers.

Today the vast majority of the world's information resides in, is derived from, and is exchanged among multiple automated systems. Critical decisions are made, and critical action is taken based on information from these systems. Therefore, the information must be accurate, correct, and timely, and be manipulated, stored, retrieved, and exchanged safely, reliably, and securely. In a time when information is considered the latest commodity, information security should be top priority. A Practical Guide to Security Engineering and Information Assurance gives you an engineering approach to information security and information assurance (IA). The book examines the impact of accidental and malicious intentional action and inaction on information security and IA. Innovative long-term vendor, technology, and application-independent strategies show you how to protect your critical systems and data from accidental and intentional action and inaction that could lead to system failure or compromise. The author presents step-by-step, in-depth processes for defining information security and assurance goals, performing vulnerability and threat analysis, implementing and verifying the effectiveness of threat control measures, and conducting accident and incident investigations. She explores real-world strategies applicable to all systems, from small systems supporting a home-based business to those of a multinational corporation, government agency, or critical infrastructure system. The information revolution has brought its share of risks. Exploring the synergy between security, safety, and reliability engineering, A Practical Guide to Security Engineering and Information Assurance consolidates and organizes current thinking about information security/IA techniques, approaches, and best practices. As this book will show you, there is considerably more to information security/IA than firewalls, encryption, and virus protection.

This handbook covers smart manufacturing development, processing, modifications, and applications. It provides a complete understanding of the recent advancements in smart manufacturing through its various enabling manufacturing technologies, and how industries and organizations can find the needed information on how to implement smart manufacturing towards sustainability of manufacturing practices. Handbook of Smart Manufacturing: Forecasting the Future of Industry 4.0 covers all related advances in manufacturing such as the integration of reverse engineering with smart manufacturing, industrial internet of things (IIoT), and artificial intelligence approaches, including Artificial Neural Network, Markov Decision Process, and Heuristics Methodology. It offers smart manufacturing methods like 4D printing, micro-manufacturing, and processing of smart materials to assist the biomedical industries in the fabrication of human prostheses and implants. The handbook goes on to discuss how to accurately predict the requirements, identify errors, and make innovation for the manufacturing process more manageable by implementing various advanced technologies and solutions into the traditional manufacturing process. Strategies and algorithms used to incorporate smart manufacturing into different sectors are also highlighted within the handbook. This handbook is an invaluable resource for stakeholders, industries, professionals, technocrats, academics, research scholars, senior graduate students, and human healthcare professionals.

An organization's employees are often more intimate with its computer system than anyone else. Many also have access to sensitive information regarding the company and its customers. This makes employees prime candidates for sabotaging a system if they become disgruntled or for selling privileged information if they become greedy. Insider Computer Fraud: An In-depth Framework for Detecting and Defending against Insider IT Attacks presents the methods, safeguards, and techniques that help protect an organization from insider computer fraud. Drawing from the author's vast experience assessing the adequacy of IT security for the banking and securities industries, the book presents a practical framework for identifying, measuring, monitoring, and controlling the risks associated with insider threats. It not only provides an analysis of application or system-related risks, it demonstrates the interrelationships that exist between an application and the IT infrastructure components it uses to transmit, process, and store sensitive data. The author also examines the symbiotic relationship between the risks, controls, threats, and action plans that should be deployed to enhance the overall information security governance processes. Increasing the awareness and understanding necessary to effectively manage the risks and controls associated with an insider threat, this book is an invaluable resource for those interested in attaining sound and best practices over the risk management process.

Divided into two major parts, Enhancing Computer Security with Smart Technology introduces the problems of computer security to researchers with a machine learning background, then introduces machine learning concepts to computer security professionals. Realizing the massive scope of these subjects, the author concentrates on problems related to the detection of intrusions through the application of machine learning methods and on the practical algorithmic aspects of machine learning and its role in security. A collection of tutorials that draw from a broad spectrum of viewpoints and experience, this volume is made up of chapters written by specialists in each subject field. It is accessible to any professional with a basic background in computer science. Following an introduction to the issue of cyber-security and cyber-trust, the book offers a broad survey of the state-of-the-art in firewall technology and of the importance of Web application security. The remainder of the book focuses on the use of machine learning methods and tools and their performance.

Most businesses are aware of the danger posed by malicious network intruders and other internal and external security threats. Unfortunately, in many cases the actions they have taken to secure people, information and infrastructure from outside attacks are inefficient or incomplete. Responding to security threats and incidents requires a competent mixture of risk management, security policies and procedures, security auditing, incident response, legal and law enforcement issues, and privacy. Critical Incident Management presents an expert overview of the elements that organizations need to address in order to prepare for and respond to network and information security violations. Written in a concise, practical style that emphasizes key points, this guide focuses on the establishment of policies and actions that prevent the loss of critical information or damage to infrastructure. CTOs, CFOs, Chief Legal Officers, and senior IT managers can rely on this book to develop plans that thwart critical security incidents. And if such incidents do occur, these executives will have a reference to help put the people and procedures in place to contain the damage and get back to business.

Expert Bytes: Computer Expertise in Forensic Documents - Players, Needs, Resources and Pitfalls -introduces computer scientists and forensic document examiners to the computer expertise of forensic documents and assists them with the design of research projects in this interdisciplinary field. This is not a textbook on how to perform the actual forensic document expertise or program expertise software, but a project design guide, an anthropological inquiry, and a technology, market, and policies review. After reading this book you will have deepened your knowledge on: What computational expertise of forensic documents is What has been done in the field so far and what the future looks like What the expertise is worth, what its public image is, and how to improve both Who is doing what in the field, where, and for how much How the expertise software functions The primary target readers are computer scientists and forensic document examiners, at the student and professional level. Paleographers, historians of science and technology, and scientific policy makers can also profit from the book. Concise and practical, featuring an attractive and functional layout design, the book is supplemented with graphical data representations, statistics, resource lists, and extensive references to facilitate further study.

MANAGEMENT OF INFORMATION SECURITY, Sixth Edition prepares you to become an information security management practitioner able to secure systems and networks in a world where continuously emerging threats, ever-present attacks and the success of criminals illustrate the weaknesses in current information technologies. You'll develop both the information security skills and practical experience that organizations are looking for as they strive to ensure more secure computing environments. The text focuses on key executive and managerial aspects of information security. It also integrates coverage of CISSP and CISM throughout to effectively prepare you for certification. Reflecting the most recent developments in the field, it includes the latest information on NIST, ISO and security governance as well as emerging concerns like Ransomware, Cloud Computing and the Internet of Things.

1. The main emphasis of this book is on the building fundamentals of IoT networks by leveraging the important and relevant concepts from Signal Processing, Communications, Networks and Machine learning. The book focuses on IoT protocols, energy harvesting, control optimization, clustering, data fusion, data analytics, localization, fog computing, privacy and security including Elliptic curve cryptography and Blockchain technology. 2. As we know, the IoT network has revolutionized the world and has innumerable real-time applications on automation. The course on IoT is highly popular among students, faculty members, industrial professionals. The course is taught in almost all colleges/universities of India and abroad. The course is now conducted by industries/joint collaboration with industry-academia too. 3. The most of the existing books in the market are theoretical in nature without ma thematical description and examples. This does not create the interest among the readers. Moreover, some essential topics of the IoT networks are also missing in the existing books. However, if we look at the proposed course contents of the books, it covers all the cutting-edge research topics into a single book

Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models' performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.

It is a major challenge to migrate very large databases from one system, say for example, to transfer critical data from Oracle to SQL Server. One has to consider several issues such as loss of data being transferred, the security of the data, the cost and effort, technical aspects of the software involved, etc. There a very few books that provide practical tools and the methodology to migrate data from one vendor to another. This book introduces the concepts in database migration with large sample databases. It provides step by step guides and screenshots for database migration tools. Many examples are shown for migrating Oracle, SQL Server and MySQL databases.

Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you'll get a chance to revisit the concepts you've covered so far. What You Will Learn Use Python programming techniques for data science Master data collections in Python Create engaging visualizations for BI systems Deploy effective strategies for gathering and cleaning data Integrate the Seaborn and Matplotlib plotting systems Who This Book Is For Developers with basic Python programming knowledge looking to adopt key strategies for data analysis and visualizations using Python.

A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis. Features Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory Focuses on methodology and results rather than formal proofs Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) Uses concrete and realistic data analysis examples to guide the reader Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges

The Charter of the United Nations was signed in 1945 by 51 countries representing all continents, paving the way for the creation of the United Nations on 24 October 1945. The Statute of the International Court of Justice forms part of the Charter. The aim of the Charter is to save humanity from war; to reaffirm human rights and the dignity and worth of the human person; to proclaim the equal rights of men and women and of nations large and small; and to promote the prosperity of all humankind. The Charter is the foundation of international peace and security.

This book is a timely and critical introduction for those interested in what data science is (and isn't), and how it should be applied. The language is conversational and the content is accessible for readers without a quantitative or computational background; but, at the same time, it is also a practical overview of the field for the more technical readers. The overarching goal is to demystify the field and teach the reader how to develop an analytical mindset instead of following recipes. The book takes the scientist's approach of focusing on asking the right question at every step as this is the single most important factor contributing to the success of a data science project. Upon finishing this book, the reader should be asking more questions than I have answered. This book is, therefore, a practising scientist's approach to explaining data science through questions and examples.

Engineering analytics is becoming a necessary skill for every engineer. Areas such as Operations Research, Simulation, and Machine Learning can be totally transformed through massive volumes of data. This book is intended to be an introduction to Engineering Analytics that can be used to improve performance tracking, customer segmentation for resource optimization, patterns and classification strategies, and logistics control towers. Basic methods in the areas of visual, descriptive, predictive, and prescriptive analytics and Big Data are introduced. Industrial case studies and example problem demonstrations are used throughout the book to reinforce the concepts and applications. The book goes on to cover visual analytics and its relationships, simulation from the respective dimensions and Machine Learning and Artificial Intelligence from different paradigms viewpoints. The book is intended for professionals wanting to work on analytical problems, for Engineering students, Researchers, Chief-Technology Officers, and Directors that work within the areas and fields of Industrial Engineering, Computer Science, Statistics, Electrical Engineering Operations Research, and Big Data.

In today's fast growing digital world, the web, mobile, social networks and other digital platforms are producing enormous amounts of data that hold intelligence and valuable information. Correctly used it has the power to create sustainable value in different forms for businesses. The commonly used term for this data is Big Data, which includes structured, unstructured and hybrid structured data. However, Big Data is of limited value unless insightful information can be extracted from the sources of data. The solution is Big Data analytics, and how managers and executives can capture value from this vast resource of information and insights. This book develops a simple framework and a non-technical approach to help the reader understand, digest and analyze data, and produce meaningful analytics to make informed decisions. It will support value creation within businesses, from customer care to product innovation, from sales and marketing to operational performance. The authors provide multiple case studies on global industries and business units, chapter summaries and discussion questions for the reader to consider and explore. Big Data for Managers also presents small cases and challenges for the reader to work on - making this a thorough and practical guide for students and managers.

*Covers not just the basics of storytelling with data but also the more complex issues of data searching, cleaning and scraping from a journalist's, rather than a computer scientist's, perspective. *Data Journalism courses are taught at BA and MA level on most Journalism degrees around the world, both as compulsory and elective modules. *Mike Reilley is a leading authority in this area- he founded the Journalist's Toolbox newsletter which shares digital and data tools and has over 51k followers on Twitter, so his name will draw a lot of attention to this book.

Enterprise Process Management Systems: Engineering Process-Centric Enterprise Systems using BPMN 2.0 proposes a process-centric paradigm to replace the traditional data-centric paradigm for Enterprise Systems (ES)--ES should be reengineered from the present data-centric enterprise architecture to process-centric process architecture to be called as Enterprise Process Management Systems (EPMS). The real significance of business processes can be understood in the context of current heightened priority on digital transformation or digitalization of enterprises. Conceiving the roadmap to realize a digitalized enterprise via the business model innovation becomes amenable only from the process-centric view of the enterprise. This pragmatic book: Introduces Enterprise Process Management Systems (EPMS) solutions that enable an agile enterprise. Describes distributed systems and Service Oriented Architecture (SOA) that paved the road to EPMS. Leverages SOA to explain the cloud-based realization of business processes in terms of Web Services. Describes how BPMN 2.0 addresses the requirements for agility by ensuring a seamless methodological path from process requirements modeling to execution and back (to enable process improvements). Presents the spreadsheet-driven Spreadsheeter Application Development (SAD) methodology for the design and development of process-centric application systems. Describes process improvement programs ranging right from disruptive programs like BPR to continuous improvement programs like lean, six sigma and TOC. Enterprise Process Management Systems: Engineering Process-Centric Enterprise Systems using BPMN 2.0 describes how BPMN 2.0 can not only capture business requirements but it can also provide the backbone of the actual solution implementation. Thus, the same diagram prepared by the business analyst to describe the business's desired To-Be process can also be used to automate the execution of that process on a modern process engine.

The world's most infamous hacker offers an insider's view of the low-tech threats to high-tech security
Kevin Mitnick's exploits as a cyber-desperado and fugitive form one of the most exhaustive FBI manhunts in history and have spawned dozens of articles, books, films, and documentaries. Since his release from federal prison, in 1998, Mitnick has turned his life around and established himself as one of the most sought-after computer security experts worldwide. Now, in The Art of Deception, the world's most notorious hacker gives new meaning to the old adage, "It takes a thief to catch a thief."
Focusing on the human factors involved with information security, Mitnick explains why all the firewalls and encryption protocols in the world will never be enough to stop a savvy grifter intent on rifling a corporate database or an irate employee determined to crash a system. With the help of many fascinating true stories of successful attacks on business and government, he illustrates just how susceptible even the most locked-down information systems are to a slick con artist impersonating an IRS agent. Narrating from the points of view of both the attacker and the victims, he explains why each attack was so successful and how it could have been prevented in an engaging and highly readable style reminiscent of a true-crime novel. And, perhaps most importantly, Mitnick offers advice for preventing these types of social engineering hacks through security protocols, training programs, and manuals that address the human element of security.

This book is designed to help practitioners and students in a wide range of construction project management professions to understand what building information modelling (BIM) and big data could mean for them and how they should prepare to work successfully on BIM-compliant projects and maintain their competencies in this essential and expanding area. In this book, the state-of-the-art information technologies that support high-profile BIM implementation are introduced, and case studies show how BIM has integrated core quantity surveying and cost management responsibilities and how big data can enable informed decision-making for cost control and cost planning. The authors' combined professional and academic experience demonstrates, with practical examples, the importance of using BIM and particularly the fusion of BIM and big data, to sharpen competitiveness in global and domestic markets. This book is a highly valuable guide for people in a wide range of construction project management and quantity surveying roles. In addition, implications for project management, facilities management, contract administration, and dispute resolution are also explored through the case studies, making this book essential reading for built environment and engineering professionals.

Information security has a major gap when cryptography is implemented. Cryptographic algorithms are well defined, key management schemes are well known, but the actual deployment is typically overlooked, ignored, or unknown. Cryptography is everywhere. Application and network architectures are typically well-documented but the cryptographic architecture is missing. This book provides a guide to discovering, documenting, and validating cryptographic architectures. Each chapter builds on the next to present information in a sequential process. This approach not only presents the material in a structured manner, it also serves as an ongoing reference guide for future use.

To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design. Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems.