Type of Publication: Journal Articles
Authors: Eitan Menahem, Asaf Shabtai, Lior Rokach, Yuval Elovici,
Title: Improving malware detection by applying multi-inducer ensemble
Name of the Journal: Computational Statistics & Data Analysis (CSDA)
Year: 2009
Volume: 53
Pages: 1483-1494
Abstract: Detection of malicious software (malware) using machine learning methods has been explored extensively to enable fast detection of newreleased 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, Na?ve 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.
Keywords: malware detection, machine-learning, ensemble learning
Last Updated: 9/10/2012 12:00:00 AM
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