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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: 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

APA Style
Moawad, A., Ebada, A.I., Al-Zoghby, A.M. (2022). A survey on visualization-based malware detection. Journal of Cyber Security, 4(3), 169-184.
Vancouver Style
Moawad A, Ebada AI, Al-Zoghby AM. A survey on visualization-based malware detection. J Cyber Secur . 2022;4(3):169-184
IEEE Style
A. Moawad, A.I. Ebada, and A.M. Al-Zoghby "A Survey on Visualization-Based Malware Detection," J. Cyber Secur. , vol. 4, no. 3, pp. 169-184. 2022.

cc 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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