Title :
How to predict e-mail viruses under uncertainty
Author :
Yoo, InSeon ; Ultes-Nitsche, U.
Author_Institution :
Dept. of Informatics, Fribourg Univ., Switzerland
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);
Conference_Titel :
Performance, Computing, and Communications, 2004 IEEE International Conference on
Print_ISBN :
0-7803-8396-6
DOI :
10.1109/PCCC.2004.1395133