Improving malware detection by applying multi-inducer ensemble

  title={Improving malware detection by applying multi-inducer ensemble},
  author={Eitan Menahem and Asaf Shabtai and Lior Rokach and Yuval Elovici},
  journal={Computational Statistics & Data Analysis},
Detection of malicious software (malware) using ma chine learning methods has been explored extensively to enable fas t detection of new released malware. The performance of these classifiers depen ds on the induction algorithms being used. In order to benefit from mul tiple different classifiers, and exploit their strengths we suggest using an ens emble method that will combine the results of the individual classifiers i nto one final result to achieve overall higher detection accuracy. In… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 34 references

N-gram-based detection of new malicious code

Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004. • 2004
View 4 Excerpts
Highly Influenced

A Theory of Learning Classificatio n Rules. Doctoral dissertation. School of computing Science, University of Technolo gy, Sydney

W. Buntine
View 5 Excerpts
Highly Influenced

Compariso n of Feature Selection and Classification Algorithms in Identifying Malicious E xecutables

D M.Caia, M. Gokhaleb, J. Theilerc
Computational Statistics and Data Analysis vol • 2007
View 1 Excerpt

Diversity in multiple classifier systems

Information Fusion • 2005
View 1 Excerpt

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