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Malware detection using machine learning
2009
2009 International Multiconference on Computer Science and Information Technology
We propose a versatile framework in which one can employ different machine learning algorithms to successfully distinguish between malware files and clean files, while aiming to minimise the number of false positives. In this paper we present the ideas behind our framework by working firstly with cascade one-sided perceptrons and secondly with cascade kernelized one-sided perceptrons. After having been successfully tested on medium-size datasets of malware and clean files, the ideas behind this
doi:10.1109/imcsit.2009.5352759
dblp:conf/imcsit/GavrilutCAC09
fatcat:ncdivdjxhnhs3kba4v2anvskda