rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Anomaly Detection Principles and Algorithms
  name:
    name part: Mehrotra, Kishan G.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Mohan, Chilukuri K.
    role:
      role term: author.
  name:
    name part: Huang, HuaMing.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXII, 217 p. 66 illus., 55 illus. in color. online resource.
  abstract: 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.
  table of contents: 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.
  note: by Kishan G. Mehrotra, Chilukuri K. Mohan, HuaMing Huang.
  subject:
    topic: Computer science
  subject:
    topic: Data mining
  subject:
    topic: Pattern recognition
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Pattern Recognition
  subject:
    topic: Security
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Terrorism, Security, and Computation
  identifier: 9783319675268
  identifier: http://dx.doi.org/10.1007/978-3-319-67526-8
  record info:
    record creation date: 171118
    record change date: 20171118175901.0
    record identifier: 978-3-319-67526-8