@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} }