• DocumentCode
    1317663
  • Title

    Adversarial Machine Learning

  • Author

    Tygar, J.D.

  • Author_Institution
    University of California, Berkeley
  • Volume
    15
  • Issue
    5
  • fYear
    2011
  • Firstpage
    4
  • Lastpage
    6
  • Abstract
    The author briefly introduces the emerging field of adversarial machine learning, in which opponents can cause traditional machine learning algorithms to behave poorly in security applications. He gives a high-level overview and mentions several types of attacks, as well as several types of defenses, and theoretical limits derived from a study of near-optimal evasion.
  • Keywords
    adversarial machine learning; computer security; intrusion detection; machine learning; spam email;
  • fLanguage
    English
  • Journal_Title
    Internet Computing, IEEE
  • Publisher
    ieee
  • ISSN
    1089-7801
  • Type

    jour

  • DOI
    10.1109/MIC.2011.112
  • Filename
    6015575
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1317663