• DocumentCode
    2584216
  • Title

    How to predict e-mail viruses under uncertainty

  • Author

    Yoo, InSeon ; Ultes-Nitsche, U.

  • Author_Institution
    Dept. of Informatics, Fribourg Univ., Switzerland
  • fYear
    2004
  • fDate
    2004
  • Firstpage
    675
  • Lastpage
    679
  • Abstract
    This paper answers (addresses) the questions on how to detect email viruses without signatures and how to determine the probability whether the mail is abnormal and how to detect virus patterns in an infected file. In order to find out relations between email viruses and detectable knowledge, we analysed propagation of email viruses and characteristics of email viruses, studied infected files´ structures and applied Bayesian networks and self-organizing maps to adaptive detection against email viruses.
  • Keywords
    belief networks; computer viruses; electronic mail; probability; self-organising feature maps; Bayesian networks; adaptive detection; e-mail viruses; infected file; self-organizing maps; virus pattern detection; Bayesian methods; Computer viruses; Filters; Informatics; Postal services; Probability; Protocols; Self organizing feature maps; Uncertainty; Viruses (medical);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Performance, Computing, and Communications, 2004 IEEE International Conference on
  • Print_ISBN
    0-7803-8396-6
  • Type

    conf

  • DOI
    10.1109/PCCC.2004.1395133
  • Filename
    1395133