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
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