Vol. 1 No. 1 (2021): Journal of Millimeterwave Communication, Optimization and Modelling
Articles

Visual Malware Detection by Deep Learning Techniques in Windows System

Malware-Hussein-PDF
  • Hussein Almusawi
Hussein Almusawi
Phd Candidate
JOMCOM Journal Cover

Published 07.11.2021

Keywords

  • Malware,
  • Convulational Neural Network,
  • Deep Learning,
  • Malware Image

Copyright (c) 2021 Hussein Almusawi

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

The number of malware is increasing dramatically day by day with the development of modern technologies such as the Internet and electronic banks, so we need advanced technology to detect this malicious software more effective than anti-virus programs that rely on the signature of malicious software, which has proven its failure in some cases. In this study, the deep learning technique which is one of the branches of artificial intelligence through Convolutional Neural Network (CNN) has been proposed and applied to dataset called Malimg which consist of 25 families and 9339 samples of malware gray scale images and these images converted from malware binary files. The result of our method has proven its efficiency by obtaining an accuracy of 96.76% in malware detection.

 

Malware-Hussein-PDF

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