LEADER 04006nam 22006015 450 001 9910254843103321 005 20200701171101.0 010 $a3-319-67526-5 024 7 $a10.1007/978-3-319-67526-8 035 $a(CKB)4100000001042039 035 $a(DE-He213)978-3-319-67526-8 035 $a(MiAaPQ)EBC5150920 035 $a(PPN)221253300 035 $a(EXLCZ)994100000001042039 100 $a20171118d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnomaly Detection Principles and Algorithms /$fby Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XXII, 217 p. 66 illus., 55 illus. in color.) 225 1 $aTerrorism, Security, and Computation,$x2197-8778 311 $a3-319-67524-9 320 $aIncludes bibliographical references and index. 327 $a1 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. 330 $aThis book provides a readable and elegant presentation of the principles of anomaly detection,providing an easy introduction for newcomers to the field. A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses. The authors also cover algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data. New ensemble anomaly detection algorithms are described, utilizing the benefits provided by diverse algorithms, each of which work well on some kinds of data. With advancements in technology and the extensive use of the internet as a medium for communications and commerce, there has been a tremendous increase in the threats faced by individuals and organizations from attackers and criminal entities. Variations in the observable behaviors of individuals (from others and from their own past behaviors) have been found to be useful in predicting potential problems of various kinds. Hence computer scientists and statisticians have been conducting research on automatically identifying anomalies in large datasets. This book will primarily target practitioners and researchers who are newcomers to the area of modern anomaly detection techniques. Advanced-level students in computer science will also find this book helpful with their studies. 410 0$aTerrorism, Security, and Computation,$x2197-8778 606 $aData mining 606 $aPattern recognition 606 $aData protection 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aSecurity$3https://scigraph.springernature.com/ontologies/product-market-codes/I28000 615 0$aData mining. 615 0$aPattern recognition. 615 0$aData protection. 615 14$aData Mining and Knowledge Discovery. 615 24$aPattern Recognition. 615 24$aSecurity. 676 $a005.8 700 $aMehrotra$b Kishan G$4aut$4http://id.loc.gov/vocabulary/relators/aut$01060422 702 $aMohan$b Chilukuri K$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aHuang$b HuaMing$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254843103321 996 $aAnomaly Detection Principles and Algorithms$92513511 997 $aUNINA