A Survey on Visualization-Based Malware Detection

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 sof...

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Bibliographic Details
Published inJournal of cyber security (Henderson, Nev.) Vol. 4; no. 3; pp. 169 - 184
Main Authors Moawad, Ahmad, Ismail Ebada, Ahmed, M. Al-Zoghby, Aya
Format Journal Article
LanguageEnglish
Published Henderson Tech Science Press 2022
Subjects
Anti-virus software
Blockchain
Computer viruses
Computers
Cybersecurity
Digital currencies
Exploitation
Image processing
Malware
Network security
Ransomware
Remote searching
Subject specialists
Visualization
Worms
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Summary: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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ISSN:2579-0064
2579-0072
2579-0064
DOI:10.32604/jcs.2022.033537