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