Building Visual Malware Dataset using VirusShare Data and Comparing Machine Learning Baseline Model to CoAtNet for Malware Classification
Roberto Bruzzese · 2024
The present work takes inspiration from the work of Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan at Google Research, Brain Team about CoAtNet . In that work it was showed that it is possible to combine the strengths from both convolution and transformer architectures, by unifying convnets and self-attention into a machine learning model. We want to apply the CoAtNet to a visual dataset of malware images and compare its performances to a baseline CNN model. For this reason we need a data set of appropriate size and format. From these needs triggers the requirement to find or generate a visual dataset of the malware images capable to measure the accuracy of the constructed model. As will be seen, the creation of a new dataset will be preferred to the search for an existing dataset. Although the visual approach has already been extensively tested in recent years, there is still a need for more customised data for the model under examination. The work described in this paper can serve as a guide to a balanced and dimensioned construction of an optimal malware visual image dataset.