@Article{jcs.2022.033537,
AUTHOR = {Ahmad Moawad, Ahmed Ismail Ebada, Aya M. Al-Zoghby},
TITLE = {A Survey on Visualization-Based Malware Detection},
JOURNAL = {Journal of Cyber Security},
VOLUME = {4},
YEAR = {2022},
NUMBER = {3},
PAGES = {169--184},
URL = {http://www.techscience.com/JCS/v4n3/51400},
ISSN = {2579-0064},
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.},
DOI = {10.32604/jcs.2022.033537}
}