| Resumen |
The amount of malicious software detected in the wild have greatly increased over the years, same as their complexity and ability to avoid detection mechanisms and techniques improved. To aid in identifying and defending against this malware, we propose a multi-dimensional and multi-level malware taxonomy, a multi-label classification scheme, a multi-label malware dataset and a tool to automate the process of analyzing, labeling, preprocessing and presentation. Alongside this, we discuss the current state of each element and future work. © 2022 IEEE. |
