04006nam 22006015 450 991025484310332120200701171101.03-319-67526-510.1007/978-3-319-67526-8(CKB)4100000001042039(DE-He213)978-3-319-67526-8(MiAaPQ)EBC5150920(PPN)221253300(EXLCZ)99410000000104203920171118d2017 u| 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierAnomaly Detection Principles and Algorithms /by Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang1st ed. 2017.Cham :Springer International Publishing :Imprint: Springer,2017.1 online resource (XXII, 217 p. 66 illus., 55 illus. in color.)Terrorism, Security, and Computation,2197-87783-319-67524-9 Includes bibliographical references and index.1 Introduction -- 2 Anomaly Detection -- 3 Distance-based Anomaly Detection Approaches -- 4 Clustering-based Anomaly Detection Approaches -- 5 Model-based Anomaly Detection Approaches -- 6 Distance and Density Based Approaches -- 7 Rank Based Approaches -- 8 Ensemble Methods -- 9 Algorithms for Time Series Data -- Datasets for Evaluation -- Datasets for Time Series Experiments.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.Terrorism, Security, and Computation,2197-8778Data miningPattern recognitionData protectionData Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XSecurityhttps://scigraph.springernature.com/ontologies/product-market-codes/I28000Data mining.Pattern recognition.Data protection.Data Mining and Knowledge Discovery.Pattern Recognition.Security.005.8Mehrotra Kishan Gauthttp://id.loc.gov/vocabulary/relators/aut1060422Mohan Chilukuri Kauthttp://id.loc.gov/vocabulary/relators/autHuang HuaMingauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910254843103321Anomaly Detection Principles and Algorithms2513511UNINA