"Clustering versus SVM for malware detection" by Usha Narra

Selected Works of Thomas H. Austin

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Clustering versus SVM for malware detection

Journal of Computer Virology and Hacking Techniques (2016)

Usha Narra, San Jose State University
Fabio Di Troia, Università degli Studi del Sannio
Visaggio Aaron Corrado, Università degli Studi del Sannio
Thomas H. Austin, San Jose State University
Mark Stamp, San Jose State University

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Abstract

Previous work has shown that cluster analysis can be used to effectively classify malware into meaningful families. In this research, we apply cluster analysis to the challenging problem of classifying previously unknown malware. We perform several experiments involving malware clustering. We compare our clustering results to those obtained when a support vector machine (SVM) is trained on the malware family. Using clustering, we are able to classify malware with an accuracy comparable to that of an SVM. An advantage of the clustering approach is that a new malware family can be classified before a model has been trained specifically for the family.

Disciplines

Computer Sciences

Publication Date

2016

DOI

10.1007/s11416-015-0253-z

Citation Information

Usha Narra, Fabio Di Troia, Visaggio Aaron Corrado, Thomas H. Austin, et al.. "Clustering versus SVM for malware detection" Journal of Computer Virology and Hacking Techniques Vol. 12 Iss. 4 (2016) p. 213 - 224
Available at: http://works.bepress.com/thomas_austin/23/