%0 Journal Article %A Cristina Vatamanu and Doina Cosovan and DragoĊ 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