The scope of this paper is to present a malware detection approach using machine learning. In this paper we will focus on windows executable files. Because of the abnormal growth of these malicious software’s we need to use different automated approaches to find theses infected files.

In this project we are going to study and implement a script used for data extraction from the PE-files to create a data set with infected and clean files, on which we are gonna train our machine learning algorithms:K-nn, XGBoost and Random Forest.

The last chapter of this paper the algorithms are tested with all the data set features. The accuracy of all algorithms is over 90%. After applying a Feature selection algorithm over the data set, the accuracy has been improved for all the learning algorithms