Intelligent detection computer viruses based on multiple classifiers

Authors:
Boyun Zhang;Jianping Yin;Jingbo Hao
Affiliations:
School of Computer Science, National University of Defense Technology, Changsha, P.R. China and Department of Computer Science, Hunan Public Security College, Changsha, P.R. China;School of Computer Science, National University of Defense Technology, Changsha, P.R. China;School of Computer Science, National University of Defense Technology, Changsha, P.R. China
Venue:
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
Year:
2007

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Abstract

In this paper, we generalize the problem of multi-classifiers combination by using modified bagging method to detect previously unknown viruses. The detection engine applies two algorithms, Support Vector Machine and BP neural network to virus detection. For SVM classifier, we extract the feature vector from the API function calls by monitor the programs. And the static feature of program, n-gram, is used in the BP neural network classifier. Finally, the D-S theory of evidence is used to combine the contribution of each individual classifier to give the final decision. Our extensive experiments have shown that the combination approach improves the performance of the individual classifier significantly. It shows that the present method could effectively be used to discriminate normal and abnormal programs.