In this paper we explore the capabilities of a framework that can use
different machine learning algorithms to successfully detect malware
files, aiming to minimize the number of false positives. We report the
results obtained in our framework, working firstly with cascades of
one-sided perceptron and kernelized one-sides perceptrons and secondly
with cascade of one-sided support vector machines.
Mendeley saves you time finding and organizing research
Choose a citation style from the tabs below