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Anomaly Detection Principles and Algorithms

2017-11-18 Computers
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Release : 2017-11-18
Genre : Computers
Kind : eBook
Book Rating : 265/5 ( reviews)

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Book Synopsis Anomaly Detection Principles and Algorithms by : Kishan G. Mehrotra

Download or read book Anomaly Detection Principles and Algorithms written by Kishan G. Mehrotra. This book was released on 2017-11-18. Available in PDF, EPUB and Kindle. Book excerpt: 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

2014-07-21 Computers
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Release : 2014-07-21
Genre : Computers
Kind : eBook
Book Rating : 181/5 ( reviews)

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Book Synopsis Practical Machine Learning: A New Look at Anomaly Detection by : Ted Dunning

Download or read book Practical Machine Learning: A New Look at Anomaly Detection written by Ted Dunning. This book was released on 2014-07-21. Available in PDF, EPUB and Kindle. Book excerpt: 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

Network Anomaly Detection

2013-06-18 Computers
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Release : 2013-06-18
Genre : Computers
Kind : eBook
Book Rating : 09X/5 ( reviews)

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Book Synopsis Network Anomaly Detection by : Dhruba Kumar Bhattacharyya

Download or read book Network Anomaly Detection written by Dhruba Kumar Bhattacharyya. This book was released on 2013-06-18. Available in PDF, EPUB and Kindle. Book excerpt: With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavi

Network Traffic Anomaly Detection and Prevention

2017-09-03 Computers
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Release : 2017-09-03
Genre : Computers
Kind : eBook
Book Rating : 889/5 ( reviews)

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Book Synopsis Network Traffic Anomaly Detection and Prevention by : Monowar H. Bhuyan

Download or read book Network Traffic Anomaly Detection and Prevention written by Monowar H. Bhuyan. This book was released on 2017-09-03. Available in PDF, EPUB and Kindle. Book excerpt: This indispensable text/reference presents a comprehensive overview on the detection and prevention of anomalies in computer network traffic, from coverage of the fundamental theoretical concepts to in-depth analysis of systems and methods. Readers will benefit from invaluable practical guidance on how to design an intrusion detection technique and incorporate it into a system, as well as on how to analyze and correlate alerts without prior information. Topics and features: introduces the essentials of traffic management in high speed networks, detailing types of anomalies, network vulnerabilities, and a taxonomy of network attacks; describes a systematic approach to generating large network intrusion datasets, and reviews existing synthetic, benchmark, and real-life datasets; provides a detailed study of network anomaly detection techniques and systems under six different categories: statistical, classification, knowledge-base, cluster and outlier detection, soft computing, and combination learners; examines alert management and anomaly prevention techniques, including alert preprocessing, alert correlation, and alert post-processing; presents a hands-on approach to developing network traffic monitoring and analysis tools, together with a survey of existing tools; discusses various evaluation criteria and metrics, covering issues of accuracy, performance, completeness, timeliness, reliability, and quality; reviews open issues and challenges in network traffic anomaly detection and prevention. This informative work is ideal for graduate and advanced undergraduate students interested in network security and privacy, intrusion detection systems, and data mining in security. Researchers and practitioners specializing in network security will also find the book to be a useful reference.

Beginning Anomaly Detection Using Python-Based Deep Learning

2019-10-10 Computers
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Release : 2019-10-10
Genre : Computers
Kind : eBook
Book Rating : 776/5 ( reviews)

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Book Synopsis Beginning Anomaly Detection Using Python-Based Deep Learning by : Sridhar Alla

Download or read book Beginning Anomaly Detection Using Python-Based Deep Learning written by Sridhar Alla. This book was released on 2019-10-10. Available in PDF, EPUB and Kindle. Book excerpt: 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 of time 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 LearnUnderstand 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

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