Anomaly Detection Principles And Algorithms

Author : Kishan G. Mehrotra,Chilukuri K. Mohan,HuaMing Huang
Publisher : Springer
Page : 217 pages
File Size : 45,7 Mb
Release : 2017-11-18
Category : Computers
ISBN : 9783319675268

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Anomaly Detection Principles and Algorithms by Kishan G. Mehrotra,Chilukuri K. Mohan,HuaMing Huang Pdf

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.

Author : Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Publisher : CRC Press
Page : 366 pages
File Size : 44,7 Mb
Release : 2013-06-18
Category : Computers
ISBN : 9781466582095

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Network Anomaly Detection by Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita Pdf

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 behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents machine learning techniques in depth to help you more effectively detect and counter network intrusion. In this book, you’ll learn about: Network anomalies and vulnerabilities at various layers The pros and cons of various machine learning techniques and algorithms A taxonomy of attacks based on their characteristics and behavior Feature selection algorithms How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance Important unresolved issues and research challenges that need to be overcome to provide better protection for networks Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.

Author : Xiaochun Wang,Xiali Wang,Mitch Wilkes
Publisher : Springer Nature
Page : 287 pages
File Size : 54,8 Mb
Release : 2020-11-24
Category : Technology & Engineering
ISBN : 9789811595196

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New Developments in Unsupervised Outlier Detection by Xiaochun Wang,Xiali Wang,Mitch Wilkes Pdf

This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors’ setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research. The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.

Author : Saira Banu
Publisher : Nova Science Publishers
Page : 177 pages
File Size : 45,7 Mb
Release : 2021
Category : Computers
ISBN : 1536193550

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Anomaly Detection by Saira Banu Pdf

When information in the data warehouse is processed, it follows a definite pattern. An unexpected deviation in the data pattern from the usual behavior is called an anomaly. The anomaly in the data is also referred to as noise, outlier, spammer, deviations, novelties and exceptions. Identification of the rare items, events, observations, patterns which raise suspension by differing significantly from the majority of data is called anomaly detection. With progress in the technologies and the widespread use of data for the purpose for business the increase in the spams faced by the individuals and the companies are increasing day by day. This noisy data has boomed as a major problem in various areas such as Internet of Things, web service, Machine Learning, Artificial Intelligence, Deep learning, Image Processing, Cloud Computing, Audio processing, Video Processing, VoIP, Data Science, Wireless Sensor etc. Identifying the anomaly data and filtering them before processing is a major challenge for the data analyst. This anomaly is unavoidable in all areas of research. This book covers the techniques and algorithms for detecting the deviated data. This book will mainly target researchers and higher graduate learners in computer science and data science.

Author : Anonim
Publisher : BoD – Books on Demand
Page : 170 pages
File Size : 42,5 Mb
Release : 2024-01-17
Category : Electronic
ISBN : 9781837690268

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Anomaly Detection by Anonim Pdf

Author : Sridhar Alla,Suman Kalyan Adari
Publisher : Apress
Page : 427 pages
File Size : 50,9 Mb
Release : 2019-10-10
Category : Computers
ISBN : 9781484251775

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Beginning Anomaly Detection Using Python-Based Deep Learning by Sridhar Alla,Suman Kalyan Adari Pdf

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

Author : Danfeng (Daphne)Yao,Xiaokui Shu,Long Cheng,Salvatore J.Stolfo
Publisher : Springer Nature
Page : 157 pages
File Size : 47,5 Mb
Release : 2022-06-01
Category : Computers
ISBN : 9783031023545

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Anomaly Detection as a Service by Danfeng (Daphne)Yao,Xiaokui Shu,Long Cheng,Salvatore J.Stolfo Pdf

Anomaly detection has been a long-standing security approach with versatile applications, ranging from securing server programs in critical environments, to detecting insider threats in enterprises, to anti-abuse detection for online social networks. Despite the seemingly diverse application domains, anomaly detection solutions share similar technical challenges, such as how to accurately recognize various normal patterns, how to reduce false alarms, how to adapt to concept drifts, and how to minimize performance impact. They also share similar detection approaches and evaluation methods, such as feature extraction, dimension reduction, and experimental evaluation. The main purpose of this book is to help advance the real-world adoption and deployment anomaly detection technologies, by systematizing the body of existing knowledge on anomaly detection. This book is focused on data-driven anomaly detection for software, systems, and networks against advanced exploits and attacks, but also touches on a number of applications, including fraud detection and insider threats. We explain the key technical components in anomaly detection workflows, give in-depth description of the state-of-the-art data-driven anomaly-based security solutions, and more importantly, point out promising new research directions. This book emphasizes on the need and challenges for deploying service-oriented anomaly detection in practice, where clients can outsource the detection to dedicated security providers and enjoy the protection without tending to the intricate details.

Author : Charu C. Aggarwal
Publisher : Springer
Page : 466 pages
File Size : 48,8 Mb
Release : 2016-12-10
Category : Computers
ISBN : 9783319475783

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Outlier Analysis by Charu C. Aggarwal Pdf

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.

Author : S. Jyothi,D. M. Mamatha,Yu-Dong Zhang,K. Srujan Raju
Publisher : Springer Nature
Page : 774 pages
File Size : 47,6 Mb
Release : 2021-09-27
Category : Technology & Engineering
ISBN : 9789811619410

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Proceedings of the 2nd International Conference on Computational and Bio Engineering by S. Jyothi,D. M. Mamatha,Yu-Dong Zhang,K. Srujan Raju Pdf

This book presents the peer-reviewed proceedings of the 2nd International Conference on Computational and Bioengineering (CBE 2020) jointly organized in virtual mode by the Department of Computer Science and the Department of BioScience & Sericulture, Sri Padmavati Mahila Visvavidyalayam (Women's University), Tirupati, Andhra Pradesh, India, during 4–5 December 2020. The book includes the latest research on advanced computational methodologies such as artificial intelligence, data mining and data warehousing, cloud computing, computational intelligence, soft computing, image processing, Internet of things, cognitive computing, wireless networks, social networks, big data analytics, machine learning, network security, computer networks and communications, bioinformatics, biocomputing/biometrics, computational biology, biomaterials, bioengineering, and medical and biomedical informatics.

Author : Ted Dunning
Publisher : Unknown
Page : 128 pages
File Size : 52,6 Mb
Release : 2014
Category : Anomaly detection (Computer security)
ISBN : 1491914157

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Practical Machine Learning by Ted Dunning Pdf

Annotation Finding Data Anomalies You Didn't Know to Look ForAnomaly 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 youre looking for. This OReilly 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 whats normal and contrast that to what you observeSet an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithmEstablish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic modelUse historical data to discover anomalies in sporadic event streams, such as web trafficLearn how to use deviations in expected behavior to trigger fraud alerts.

Author : Charu C. Aggarwal,Saket Sathe
Publisher : Springer
Page : 276 pages
File Size : 40,5 Mb
Release : 2017-04-06
Category : Computers
ISBN : 9783319547657

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Outlier Ensembles by Charu C. Aggarwal,Saket Sathe Pdf

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.

Author : Hefin Rhys
Publisher : Simon and Schuster
Page : 535 pages
File Size : 50,9 Mb
Release : 2020-03-20
Category : Computers
ISBN : 9781638350170

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Machine Learning with R, the tidyverse, and mlr by Hefin Rhys Pdf

Summary Machine learning (ML) is a collection of programming techniques for discovering relationships in data. With ML algorithms, you can cluster and classify data for tasks like making recommendations or fraud detection and make predictions for sales trends, risk analysis, and other forecasts. Once the domain of academic data scientists, machine learning has become a mainstream business process, and tools like the easy-to-learn R programming language put high-quality data analysis in the hands of any programmer. Machine Learning with R, the tidyverse, and mlr teaches you widely used ML techniques and how to apply them to your own datasets using the R programming language and its powerful ecosystem of tools. This book will get you started! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning with R, the tidyverse, and mlr gets you started in machine learning using R Studio and the awesome mlr machine learning package. This practical guide simplifies theory and avoids needlessly complicated statistics or math. All core ML techniques are clearly explained through graphics and easy-to-grasp examples. In each engaging chapter, you’ll put a new algorithm into action to solve a quirky predictive analysis problem, including Titanic survival odds, spam email filtering, and poisoned wine investigation. What's inside Using the tidyverse packages to process and plot your data Techniques for supervised and unsupervised learning Classification, regression, dimension reduction, and clustering algorithms Statistics primer to fill gaps in your knowledge About the reader For newcomers to machine learning with basic skills in R. About the author Hefin I. Rhys is a senior laboratory research scientist at the Francis Crick Institute. He runs his own YouTube channel of screencast tutorials for R and RStudio. Table of contents: PART 1 - INTRODUCTION 1.Introduction to machine learning 2. Tidying, manipulating, and plotting data with the tidyverse PART 2 - CLASSIFICATION 3. Classifying based on similarities with k-nearest neighbors 4. Classifying based on odds with logistic regression 5. Classifying by maximizing separation with discriminant analysis 6. Classifying with naive Bayes and support vector machines 7. Classifying with decision trees 8. Improving decision trees with random forests and boosting PART 3 - REGRESSION 9. Linear regression 10. Nonlinear regression with generalized additive models 11. Preventing overfitting with ridge regression, LASSO, and elastic net 12. Regression with kNN, random forest, and XGBoost PART 4 - DIMENSION REDUCTION 13. Maximizing variance with principal component analysis 14. Maximizing similarity with t-SNE and UMAP 15. Self-organizing maps and locally linear embedding PART 5 - CLUSTERING 16. Clustering by finding centers with k-means 17. Hierarchical clustering 18. Clustering based on density: DBSCAN and OPTICS 19. Clustering based on distributions with mixture modeling 20. Final notes and further reading

Author : John Macintyre,Lazaros Iliadis,Ilias Maglogiannis,Chrisina Jayne
Publisher : Springer
Page : 546 pages
File Size : 50,7 Mb
Release : 2019-05-14
Category : Computers
ISBN : 9783030202576

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Engineering Applications of Neural Networks by John Macintyre,Lazaros Iliadis,Ilias Maglogiannis,Chrisina Jayne Pdf

This book constitutes the refereed proceedings of the 19th International Conference on Engineering Applications of Neural Networks, EANN 2019, held in Xersonisos, Crete, Greece, in May 2019. The 35 revised full papers and 5 revised short papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on AI in energy management - industrial applications; biomedical - bioinformatics modeling; classification - learning; deep learning; deep learning - convolutional ANN; fuzzy - vulnerability - navigation modeling; machine learning modeling - optimization; ML - DL financial modeling; security - anomaly detection; 1st PEINT workshop.

Author : Guilherme Diaz-Bérrio
Publisher : Packt Publishing Ltd
Page : 452 pages
File Size : 53,9 Mb
Release : 2024-05-10
Category : Computers
ISBN : 9781801813839

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Data Analytics for Marketing by Guilherme Diaz-Bérrio Pdf

Conduct data-driven marketing research and analysis with hands-on examples using Python by leveraging open-source tools and libraries Key Features Analyze marketing data using proper statistical techniques Use data modeling and analytics to understand customer preferences and enhance strategies without complex math Implement Python libraries like DoWhy, Pandas, and Prophet in a business setting with examples and use cases Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost marketing professionals are familiar with various sources of customer data that promise insights for success. There are extensive sources of data, from customer surveys to digital marketing data. Moreover, there is an increasing variety of tools and techniques to shape data, from small to big data. However, having the right knowledge and understanding the context of how to use data and tools is crucial. In this book, you’ll learn how to give context to your data and turn it into useful information. You’ll understand how and where to use a tool or dataset for a specific question, exploring the "what and why questions" to provide real value to your stakeholders. Using Python, this book will delve into the basics of analytics and causal inference. Then, you’ll focus on visualization and presentation, followed by understanding guidelines on how to present and condense large amounts of information into KPIs. After learning how to plan ahead and forecast, you’ll delve into customer analytics and insights. Finally, you’ll measure the effectiveness of your marketing efforts and derive insights for data-driven decision-making. By the end of this book, you’ll understand the tools you need to use on specific datasets to provide context and shape your data, as well as to gain information to boost your marketing efforts.What you will learn Understand the basic ideas behind the main statistical models used in marketing analytics Apply the right models and tools to a specific analytical question Discover how to conduct causal inference, experimentation, and statistical modeling with Python Implement common open source Python libraries for specific use cases with immediately applicable code Analyze customer lifetime data and generate customer insights Go through the different stages of analytics, from descriptive to prescriptive Who this book is for This book is for data analysts and data scientists working in a marketing team supporting analytics and marketing research, who want to provide better insights that lead to data-driven decision-making. Prior knowledge of Python, data analysis, and statistics is required to get the most out of this book.

Author : Ngoc Thanh Nguyen,Tien Khoa Tran,Ualsher Tukayev,Tzung-Pei Hong,Bogdan Trawiński,Edward Szczerbicki
Publisher : Springer Nature
Page : 766 pages
File Size : 55,5 Mb
Release : 2022-12-08
Category : Computers
ISBN : 9783031219672

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Intelligent Information and Database Systems by Ngoc Thanh Nguyen,Tien Khoa Tran,Ualsher Tukayev,Tzung-Pei Hong,Bogdan Trawiński,Edward Szczerbicki Pdf

This book constitutes the refereed proceedings of the 14th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2022, held Ho Chi Minh City, Vietnam in November 2022. The 113 full papers accepted for publication in these proceedings were carefully reviewed and selected from 406 submissions. The papers of the 2 volume-set are organized in the following topical sections: data mining and machine learning methods, advanced data mining techniques and applications, intelligent and contextual systems, natural language processing, network systems and applications, computational imaging and vision, decision support and control systems, and data modeling and processing for industry 4.0. The accepted and presented papers focus on new trends and challenges facing the intelligent information and database systems community.