Volume 14, Number 3

Detecting Malware in Portable Executable Files using Machine Learning Approach


Tuan Nguyen Kim1, Ha Nguyen Hoang2 and Nguyen Tran Truong Thien1, 1Duy Tan University, Vietnam, 2University of Sciences, Hue University, Vietnam


There have been many solutions proposed to increase the ability to detection of malware in executable files in general and in Portable Executable files in particular. In this paper, we rely on the PE header structure of Portable Executablefiles to propose another approach in using Machine learning to classify these files, as malware files or benign files. Experimental results show that the proposed approach still uses the Random Forest algorithm for the classification problem but the accuracy and execution time are improved compared to some recent publications (accuracy reaches 99.71%).


PE file, PE header, Feature, Malware, Random Forest Algorithm.