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Improving malware detection by applying multi-inducer ensemble

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  • Menahem, Eitan
  • Shabtai, Asaf
  • Rokach, Lior
  • Elovici, Yuval

Abstract

Detection of malicious software (malware) using machine learning methods has been explored extensively to enable fast detection of new released malware. The performance of these classifiers depends on the induction algorithms being used. In order to benefit from multiple different classifiers, and exploit their strengths we suggest using an ensemble method that will combine the results of the individual classifiers into one final result to achieve overall higher detection accuracy. In this paper we evaluate several combining methods using five different base inducers (C4.5 Decision Tree, Naïve Bayes, KNN, VFI and OneR) on five malware datasets. The main goal is to find the best combining method for the task of detecting malicious files in terms of accuracy, AUC and Execution time.

Suggested Citation

  • Menahem, Eitan & Shabtai, Asaf & Rokach, Lior & Elovici, Yuval, 2009. "Improving malware detection by applying multi-inducer ensemble," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1483-1494, February.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:1483-1494
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    References listed on IDEAS

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    1. Cai, D. Michael & Gokhale, Maya & Theiler, James, 2007. "Comparison of feature selection and classification algorithms in identifying malicious executables," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3156-3172, March.
    2. Moskovitch, Robert & Elovici, Yuval & Rokach, Lior, 2008. "Detection of unknown computer worms based on behavioral classification of the host," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4544-4566, May.
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    Cited by:

    1. Rokach, Lior, 2009. "Collective-agreement-based pruning of ensembles," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1015-1026, February.
    2. Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.

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