Improving malware detection by applying multi-inducer ensemble

DOI10.1016/j.csda.2008.10.015zbMath1452.62098OpenAlexW2042742130WikidataQ60142433 ScholiaQ60142433MaRDI QIDQ961294

Yuval Elovici, Eitan Menahem, Lior Rokach, Asaf Shabtai

Publication date: 30 March 2010

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2008.10.015

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Collective-agreement-based pruning of ensembles ⋮ Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography

Uses Software

• C4.5

Cites Work

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• Is combining classifiers with stacking better than selecting the best one?
• Comparison of feature selection and classification algorithms in identifying malicious executables
• Detection of unknown computer worms based on behavioral classification of the host
• Very simple classification rules perform well on most commonly used datasets

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