Comparison of feature selection and classification algorithms in identifying malicious executables

DOI10.1016/j.csda.2006.09.005zbMath1161.62459OpenAlexW1996209007MaRDI QIDQ1019947

James Theiler, D. Michael Cai, Maya B. Gokhale

Publication date: 29 May 2009

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

Full work available at URL: https://doi.org/10.1016/j.csda.2006.09.005

zbMATH Keywords

feature selectionsupport vector machineemail filteringmalicious executable fileNaïve Bayes classifier

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Communication networks in operations research (90B18) Network design and communication in computer systems (68M10) Applications of statistics (62P99)

Related Items

Improving malware detection by applying multi-inducer ensemble ⋮ Detection of unknown computer worms based on behavioral classification of the host

Uses Software

• SVMlight
• LIBSVM

Cites Work

• Wrappers for feature subset selection
• Support-vector networks
• Improvements to Platt's SMO Algorithm for SVM Classifier Design
• 10.1162/153244303322753670
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