TY  - EJOU
AU  - Moawad, Ahmad 
AU  - Ebada, Ahmed Ismail 
AU  - Al-Zoghby, Aya M. 

TI  - A Survey on Visualization-Based Malware Detection
T2  - Journal of Cyber Security

PY  - 2022
VL  - 4
IS  - 3
SN  - 2579-0064

AB  - 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.
KW  - Malware detection; malware image; malware classification; visualization-based detection; survey

DO  - 10.32604/jcs.2022.033537