@article{(Open Science Index):https://publications.waset.org/pdf/10001357,
	  title     = {A Comparative Study of Malware Detection Techniques Using Machine Learning Methods},
	  author    = {Cristina Vatamanu and  Doina Cosovan and  DragoĊŸ GavriluĊ£ and  Henri Luchian},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {9},
	  number    = {5},
	  year      = {2015},
	  pages     = {1150 - 1157},
	  ee        = {https://publications.waset.org/pdf/10001357},
	  url   	= {https://publications.waset.org/vol/101},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 101, 2015},