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, me 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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Boyun, Z., Jianping, Y., & Jingbo, H. (2007). Intelligent detection computer viruses based on multiple classifiers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4611 LNCS, pp. 1181–1190). Springer Verlag. https://doi.org/10.1007/978-3-540-73549-6_115
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