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: Adversarial machine learning
  name:
    name part: Joseph, Anthony D.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Nelson, Blaine
    role:
      role term: author.
  name:
    name part: Rubinstein, Benjamin I. P.
    role:
      role term: author.
  name:
    name part: Tygar, J. D.
    role:
      role term: author.
  name:
    name part: Cambridge University Press
  type of resource: text
  genre: bibliography
  genre: Electronic books.
  origin info:
    place:
      place term: enk
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  note: Anthony D. Joseph, University of California, Berkeley, Blaine Nelson, Google, Benjamin I.P. Rubinstein, University of Melbourne, J.D. Tygar, University of California, Berkeley.
  note: "Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race"-- Provided by publisher.
  note: Electronic resource.
  note: Includes bibliographical references and index.
  subject:
    topic: Machine learning
  subject:
    topic: Computer security
  classification: Q325.5 .J69 2017
  classification: 006.3/1
  classification: COM053000
  related item:
    identifier: (DLC) 2017026016
  identifier: 9781107338548
  identifier: 1107338549
  identifier: 9781107043466 (hardback)
  identifier: 1107043468 (hardback)
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: NhCcYBP
    record creation date: 190327
    record change date: 20190718163159.0
    record identifier: in00004060105