You can view the full text of this article for free using the link below.

Title: Malware detection approach based on deep convolutional neural networks

Authors: Hoda El Merabet; Abderrahmane Hajraoui

Addresses: Department of Physics, Faculty of Science, Abdelmalek Essaadi University, Tetuan, Morocco ' Department of Physics, Faculty of Science, Abdelmalek Essaadi University, Tetuan, Morocco

Abstract: Malware detection field becomes more valuable nowadays regarding the continuously growing number of malware codes emerging everyday. Besides, machine learning techniques have been widely used in various fields. For the purpose of employing machine learning in malware detection, an executable file should be represented by its features. Therefore, a dataset of labelled benign and malicious files is considered. Then, the developers extract the appropriate features to their model from each file. These features are displayed as inputs to a machine learning classifier. In previous researches, multiple features and classifiers were adopted in different combinations for a better classification. In this paper, we have been interested to PE header fields' features, and a deep convolutional neural network for classification. We extracted the bytes of the PE header fields' values and fed them to our model as greyscale images. Our model is constituted of 31 consecutive convolutional layers. The model was trained on the train dataset, and finally tested on the test dataset. The results were impressive reaching a test accuracy of 97.85%.

Keywords: convolutional neural networks; residual networks; deep learning; PE features; machine learning; malware detection.

DOI: 10.1504/IJICS.2023.128022

International Journal of Information and Computer Security, 2023 Vol.20 No.1/2, pp.145 - 157

Accepted: 15 Oct 2021
Published online: 04 Jan 2023 *

Full-text access for editors Full-text access for subscribers Free access Comment on this article