International Journal of Network Security & Its Applications (IJNSA) - ERA, WJCI Indexed

International Journal of Network Security & Its Applications (IJNSA) - ERA, WJCI Indexed

ISSN: 0974 - 9330 (Online); 0975 - 2307 (Print)

Webpage URL: https://airccse.org/journal/ijnsa.html

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

Abstract

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 Executable files 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%).

Keywords

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

Abstract URL: http://aircconline.com/abstract/ijnsa/v14n3/14322ijnsa02.html

Original Source URL: https://aircconline.com/ijnsa/V14N3/14322ijnsa02.pdf

Volume URL: https://airccse.org/journal/jnsa22_current.html