On the Design of Supervised Binary Classifiers for Malware Detection Using Portable Executable Files

Abstract

Executable files such as .exe, .bat, .msi etc. are used to install the software in Windows-based machines. However, downloading these files from untrusted sources may have a chance of having maliciousness. Moreover, these executables are intelligently modified by the anomalous user to bypass antivirus definitions. In this paper, we propose a method to detect malicious executables by analyzing Portable Executable (PE) files extracted from executable files. We trained a supervised binary classifier using features extracted from the PE files of normal and malicious executables. We experimented our method on a large publicly available dataset and reported more than 95% of classification accuracy.