Anomaly Detection Principles And Algorithms

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Anomaly Detection Principles And Algorithms

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Author : Kishan G. Mehrotra
language : en
Publisher: Springer
Release Date : 2017-11-18

Anomaly Detection Principles And Algorithms written by Kishan G. Mehrotra and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-18 with Computers categories.

This book provides a readable and elegant presentation of the principles of anomaly detection,providing an easy introduction for newcomers to the field. A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses. The authors also cover algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data. New ensemble anomaly detection algorithms are described, utilizing the benefits provided by diverse algorithms, each of which work well on some kinds of data. With advancements in technology and the extensive use of the internet as a medium for communications and commerce, there has been a tremendous increase in the threats faced by individuals and organizations from attackers and criminal entities. Variations in the observable behaviors of individuals (from others and from their own past behaviors) have been found to be useful in predicting potential problems of various kinds. Hence computer scientists and statisticians have been conducting research on automatically identifying anomalies in large datasets. This book will primarily target practitioners and researchers who are newcomers to the area of modern anomaly detection techniques. Advanced-level students in computer science will also find this book helpful with their studies.

Anomaly Detection Principles And Algorithms

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Author : Kishan G. Mehrotra
language : en
Publisher:
Release Date : 2017

Anomaly Detection Principles And Algorithms written by Kishan G. Mehrotra and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Anomaly detection (Computer security) categories.

This book provides a readable and elegant presentation of the principles of anomaly detection, providing an easy introduction for newcomers to the field. A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses. The authors also cover algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data. New ensemble anomaly detection algorithms are described, utilizing the benefits provided by diverse algorithms, each of which work well on some kinds of data. With advancements in technology and the extensive use of the internet as a medium for communications and commerce, there has been a tremendous increase in the threats faced by individuals and organizations from attackers and criminal entities. Variations in the observable behaviors of individuals (from others and from their own past behaviors) have been found to be useful in predicting potential problems of various kinds. Hence computer scientists and statisticians have been conducting research on automatically identifying anomalies in large datasets. This book will primarily target practitioners and researchers who are newcomers to the area of modern anomaly detection techniques. Advanced-level students in computer science will also find this book helpful with their studies.

Practical Machine Learning A New Look At Anomaly Detection

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Author : Ted Dunning
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2014-07-21

Practical Machine Learning A New Look At Anomaly Detection written by Ted Dunning and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-21 with Computers categories.

Finding Data Anomalies You Didn't Know to Look For Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what “suspects” you’re looking for. This O’Reilly report uses practical examples to explain how the underlying concepts of anomaly detection work. From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The concepts described in this report will help you tackle anomaly detection in your own project. Use probabilistic models to predict what’s normal and contrast that to what you observe Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model Use historical data to discover anomalies in sporadic event streams, such as web traffic Learn how to use deviations in expected behavior to trigger fraud alerts

Audit Analytics In The Financial Industry

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Author : Jun Dai
language : en
Publisher: Emerald Group Publishing
Release Date : 2019-10-28

Audit Analytics In The Financial Industry written by Jun Dai and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-28 with Business & Economics categories.

Split into six parts, contributors explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry. Chapters include topics such as fraud risks in the credit card sector, clustering techniques, fraud and anomaly detection, and using Audit Analytics to assess risk in the lawsuit and payment processes.

Beginning Anomaly Detection Using Python Based Deep Learning

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Author : Sridhar Alla
language : en
Publisher: Apress
Release Date : 2019-10-10

Beginning Anomaly Detection Using Python Based Deep Learning written by Sridhar Alla and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-10 with Computers categories.

Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applications of the following deep learning models to anomaly detection: various types of Autoencoders, Restricted Boltzmann Machines, RNNs & LSTMs, and Temporal Convolutional Networks. The book explores unsupervised and semi-supervised anomaly detection along with the basics oftime series-based anomaly detection. By the end of the book you will have a thorough understanding of the basic task of anomaly detection as well as an assortment of methods to approach anomaly detection, ranging from traditional methods to deep learning. Additionally, you are introduced to Scikit-Learn and are able to create deep learning models in Keras and PyTorch. What You Will Learn Understand what anomaly detection is and why it is important in today's world Become familiar with statistical and traditional machine learning approaches to anomaly detection using Scikit-Learn Know the basics of deep learning in Python using Keras and PyTorch Be aware of basic data science concepts for measuring a model's performance: understand what AUC is, what precision and recall mean, and more Apply deep learning to semi-supervised and unsupervised anomaly detection Who This Book Is For Data scientists and machine learning engineers interested in learning the basics of deep learning applications in anomaly detection

Outlier Ensembles

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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2017-04-06

Outlier Ensembles written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-06 with Computers categories.

This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.

Artificial Intelligence And Security

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Author : Xingming Sun
language : en
Publisher: Springer Nature
Release Date : 2021-07-09

Artificial Intelligence And Security written by Xingming Sun and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-09 with Computers categories.

This two-volume set of LNCS 12736-12737 constitutes the refereed proceedings of the 7th International Conference on Artificial Intelligence and Security, ICAIS 2021, which was held in Dublin, Ireland, in July 2021. The conference was formerly called “International Conference on Cloud Computing and Security” with the acronym ICCCS. The total of 93 full papers and 29 short papers presented in this two-volume proceedings was carefully reviewed and selected from 1013 submissions. Overall, a total of 224 full and 81 short papers were accepted for ICAIS 2021; the other accepted papers are presented in CCIS 1422-1424. The papers were organized in topical sections as follows: Part I: Artificial intelligence; and big data Part II: Big data; cloud computing and security; encryption and cybersecurity; information hiding; IoT security; and multimedia forensics

Machine Learning Principles Algorithms And Tools

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Author : Dr Saroj Kumar Nanda
language : en
Publisher: Addition Publishing House
Release Date : 2024-12-02

Machine Learning Principles Algorithms And Tools written by Dr Saroj Kumar Nanda and has been published by Addition Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-02 with Antiques & Collectibles categories.

Machine learning is reshaping the world, powering advancements in artificial intelligence, automation, and data-driven decision-making. As industries increasingly rely on intelligent systems, understanding how machines learn from data has become more essential than ever. This book is designed to introduce readers to the fundamental principles of machine learning in a structured and accessible manner. It breaks down complex concepts into easy-to-understand explanations, guiding readers through the process of building intelligent systems. Whether you are a student, a professional, or simply curious about AI, this book provides a solid foundation to grasp the core ideas behind machine learning. The significance of machine learning extends beyond just technology; it influences healthcare, finance, marketing, and various other fields. By understanding its principles, individuals and organizations can unlock new opportunities, optimize processes, and make smarter predictions. This book aims to bridge the gap between theoretical understanding and practical implementation, encouraging readers to think critically and explore real-world applications. As you navigate through the chapters, you will discover how machines analyze patterns, adapt to data, and improve over time. The journey into machine learning is both exciting and challenging, but with the right approach, it can be highly rewarding. This book serves as a stepping stone for anyone eager to explore the potential of intelligent systems and how they shape the future.

Artificial Intelligence And Evolutionary Algorithms In Engineering Systems

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Author : L. Padma Suresh
language : en
Publisher: Springer
Release Date : 2014-11-01

Artificial Intelligence And Evolutionary Algorithms In Engineering Systems written by L. Padma Suresh and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-01 with Technology & Engineering categories.

The book is a collection of high-quality peer-reviewed research papers presented in Proceedings of International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (ICAEES 2014) held at Noorul Islam Centre for Higher Education, Kumaracoil, India. These research papers provide the latest developments in the broad area of use of artificial intelligence and evolutionary algorithms in engineering systems. The book discusses wide variety of industrial, engineering and scientific applications of the emerging techniques. It presents invited papers from the inventors/originators of new applications and advanced technologies.

Control Performance Assessment Theoretical Analyses And Industrial Practice

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Author : Paweł D. Domański
language : en
Publisher: Springer Nature
Release Date : 2019-09-01

Control Performance Assessment Theoretical Analyses And Industrial Practice written by Paweł D. Domański and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-01 with Technology & Engineering categories.

This book presents a comprehensive review of currently available Control Performance Assessment methods. It covers a broad range of classical and modern methods, with a main focus on assessment practice, and is intended to help practitioners learn and properly perform control assessment in the industrial reality. Further, it offers an educational guide for control engineers, who are currently in high demand in the industry. The book consists of three main parts. Firstly, a comprehensive review of available approaches is presented and discussed. The classical canon methods are extended with a discussion of nonlinear and complex alternative measures using non-Gaussian statistics, persistence and fractional calculations. Secondly, the methods’ applicability aspects are visualized with the aid of computer simulations, covering the most popular control philosophies used in the process industry. Lastly, a critical review of the methods discussed, on the basis of real-world industrial examples, rounds out the coverage.