Open Access

ARTICLE

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. https://doi.org/10.32604/jcs.2022.033537

Received 01 September 2022; Accepted 02 October 2022; Issue published 01 February 2023

Abstract

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.

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Keywords

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