DocumentCode :
3132994
Title :
A study on the intelligent method for detection of computer viruses
Author :
Ren, Limin
Author_Institution :
Tianjin Inst. of Urban Constr., Tianjin, China
Volume :
2
fYear :
2011
fDate :
20-21 Aug. 2011
Firstpage :
370
Lastpage :
373
Abstract :
This paper makes a virus detection study based on the D-S theory of evidence, which applies to two types of classifiers, support vector machines and probabilistic neural networks to detect the virus. Then, the D-S theory of evidence is used to combine the contribution of each individual classifier to obtain the final decision. The experiment tests and result analyses demonstrate that it is efficient for unknown viruses and variant viruses to improve accuracy rate of integration virus detector by using D-S theory to create the isomeric classifier.
Keywords :
computer viruses; inference mechanisms; neural nets; pattern classification; support vector machines; D-S theory of evidence; computer virus detection; intelligent method; isomeric classifier; probabilistic neural networks; support vector machines; Bagging; Classification algorithms; Machine learning; Probabilistic logic; Support vector machines; Training; Viruses (medical); D-S theory of evidence; classifier; computer viruses; credit distribution; virus detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing, Control and Industrial Engineering (CCIE), 2011 IEEE 2nd International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-9599-3
Type :
conf
DOI :
10.1109/CCIENG.2011.6008141
Filename :
6008141
Link To Document :
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