• DocumentCode
    1862762
  • Title

    PAAKL: Password Authentication Using Behavioral Metrics

  • Author

    Toptsis, Anestis A. ; Majonis, Joshua

  • Author_Institution
    Dept. of Comput. Sci. & Eng., York Univ., Toronto, ON, Canada
  • fYear
    2010
  • fDate
    19-23 July 2010
  • Firstpage
    351
  • Lastpage
    356
  • Abstract
    We present PAAKL, a method for password authentication. In addition to checking for a password´s correctness, PAAKL performs authentication by verifying that password using behavioral metrics extracted from the typing style of the rightful owner of a computer account. As such, PAAKL will deny access to users who have knowledge of a password but their typing style of that password is different from the typing style of the rightful owner. We implement and test our method with actual users. Our results indicate that the rightful owners of a password self-authenticate 92.5% of the time while the intruders - users that know the password of a rightful owner but are not rightful owners themselves, have 0% success in gaining access to a system.
  • Keywords
    authorisation; behavioural sciences; software agents; PAAKL; artificial k-line; behavioral metrics; computer account; password authentication; rightful owner; typing style; user access denied; Artificial intelligence; Artificial neural networks; Authentication; Companies; Delay; Pattern matching; Training; Artificial K-lines; Password authenticatiom;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Software and Applications Conference (COMPSAC), 2010 IEEE 34th Annual
  • Conference_Location
    Seoul
  • ISSN
    0730-3157
  • Print_ISBN
    978-1-4244-7512-4
  • Electronic_ISBN
    0730-3157
  • Type

    conf

  • DOI
    10.1109/COMPSAC.2010.70
  • Filename
    5676281