%0 Journal Article
	%A Cristina Vatamanu and  Doina Cosovan and  DragoĊŸ GavriluĊ£ and  Henri Luchian
	%D 2015
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 101, 2015
	%T A Comparative Study of Malware Detection Techniques Using Machine Learning Methods
	%U https://publications.waset.org/pdf/10001357
	%V 101
	%X In the past few years, the amount of malicious software
increased exponentially and, therefore, machine learning algorithms
became instrumental in identifying clean and malware files through
(semi)-automated classification. When working with very large
datasets, the major challenge is to reach both a very high malware
detection rate and a very low false positive rate. Another challenge
is to minimize the time needed for the machine learning algorithm to
do so. This paper presents a comparative study between different
machine learning techniques such as linear classifiers, ensembles,
decision trees or various hybrids thereof. The training dataset consists
of approximately 2 million clean files and 200.000 infected files,
which is a realistic quantitative mixture. The paper investigates the
above mentioned methods with respect to both their performance
(detection rate and false positive rate) and their practicability.
	%P 1150 - 1157