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A Survey on Visualization-Based Malware Detection

Ahmad Moawad*, Ahmed Ismail Ebada, Aya M. Al-Zoghby

Computer Science Department, Faculty of Computers and Artificial Intelligence, Damietta, New Damietta, 34517, Egypt

* Corresponding Author: Ahmad Moawad. Email:

Journal of Cyber Security 2022, 4(3), 169-184.


In computer security, the number of malware threats is increasing and causing damage to systems for individuals or organizations, necessitating a new detection technique capable of detecting a new variant of malware more efficiently than traditional anti-malware methods. Traditional anti-malware software cannot detect new malware variants, and conventional techniques such as static analysis, dynamic analysis, and hybrid analysis are time-consuming and rely on domain experts. Visualization-based malware detection has recently gained popularity due to its accuracy, independence from domain experts, and faster detection time. Visualization-based malware detection uses the image representation of the malware binary and applies image processing techniques to the image. This paper aims to provide readers with a comprehensive understanding of malware detection and focuses on visualization-based malware detection.


Cite This Article

A. Moawad, A. I. Ebada and A. M. Al-Zoghby, "A survey on visualization-based malware detection," Journal of Cyber Security, vol. 4, no.3, pp. 169–184, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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