Malware Detection Using a Machine-Learning Based Approach

Authors

  • Safa Rkhouya National School of Applied Sciences (ENSA) Ibn Tofail University, Kenitra, Morocco
  • Khalid Chougdali Engineering Science Laboratory, National School of Applied Sciences (ENSA) Ibn Tofail University, Kenitra, Morocco

DOI:

https://doi.org/10.52502/ijitas.v3i4.172

Keywords:

Malware Classification, PE files, SVM, Machine Learning, Decision Tree, Gradient Boosting, Random Forest

Abstract

The purpose of this research work is to study the usage of machine learning in detecting malware. This paper presents a versatile framework, in which a dataset of more than 130000 files has been analyzed, to train and test four machine learning algorithms: Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosting; The performance of each algorithm in malware classification, has been studied based on the: Accuracy, execution time, rate of false positives and false negatives, and area under the Receiver Operating Characteristic curve.

Published

2021-10-25

How to Cite

[1]
S. Rkhouya and K. . Chougdali, “Malware Detection Using a Machine-Learning Based Approach”, IJITAS, vol. 3, no. 4, pp. 167-171, Oct. 2021.