• DocumentCode
    2058205
  • Title

    PhishZoo: Detecting Phishing Websites by Looking at Them

  • Author

    Afroz, Sadia ; Greenstadt, Rachel

  • Author_Institution
    Dept. of Comput. Sci., Drexel Univ., Philadelphia, PA, USA
  • fYear
    2011
  • fDate
    18-21 Sept. 2011
  • Firstpage
    368
  • Lastpage
    375
  • Abstract
    Phishing is a security attack that involves obtaining sensitive or otherwise private data by presenting oneself as a trustworthy entity. Phishers often exploit users´ trust on the appearance of a site by using web pages that are visually similar to an authentic site. This paper proposes a phishing detection approach - PhishZoo - that uses profiles of trusted websites´ appearances to detect phishing. Our approach provides similar accuracy to blacklisting approaches (96%), with the advantage that it can classify zero-day phishing attacks and targeted attacks against smaller sites (such as corporate intranets). A key contribution of this paper is that it includes a performance analysis and a framework for making use of computer vision techniques in a practical way.
  • Keywords
    Web sites; computer crime; computer vision; PhishZoo; blacklisting approaches; computer vision techniques; phishing Websites; security attack; trustworthy entity; zero-day phishing attacks; Accuracy; Browsers; Electronic mail; Feature extraction; HTML; Image matching; Visualization; Phishing; Security; Social engineering attacks; Website matching;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Semantic Computing (ICSC), 2011 Fifth IEEE International Conference on
  • Conference_Location
    Palo Alto, CA
  • Print_ISBN
    978-1-4577-1648-5
  • Electronic_ISBN
    978-0-7695-4492-2
  • Type

    conf

  • DOI
    10.1109/ICSC.2011.52
  • Filename
    6061361