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

• Record Nr.: UNINA9910254843103321
• Autore: Mehrotra Kishan G
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
• ISBN: 3-319-67526-5
• Edizione: [1st ed. 2017.]
• Descrizione fisica: 1 online resource (XXII, 217 p. 66 illus., 55 illus. in color.)
• Collana: Terrorism, Security, and Computation, , 2197-8778
• Disciplina: 005.8
• Soggetti: Data mining; Pattern recognition; Data protection; Data Mining and Knowledge Discovery; Pattern Recognition; Security
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.
• Sommario/riassunto: 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.