  To date, a large number of research papers have been written on the
classification of malware, its identification, classification into different
families and the distinction between malware and goodware. These works have
been based on captured malware samples and have attempted to analyse malware
and goodware using various techniques, including techniques from the field of
artificial intelligence. For example, neural networks have played a significant
role in these classification methods. Some of this work also deals with
analysing malware using its visualisation. These works usually convert malware
samples capturing the structure of malware into image structures, which are
then the object of image processing. In this paper, we propose a very
unconventional and novel approach to malware visualisation based on dynamic
behaviour analysis, with the idea that the images, which are visually very
interesting, are then used to classify malware concerning goodware. Our
approach opens an extensive topic for future discussion and provides many new
directions for research in malware analysis and classification, as discussed in
conclusion. The results of the presented experiments are based on a database of
6 589 997 goodware, 827 853 potentially unwanted applications and 4 174 203
malware samples provided by ESET and selected experimental data (images,
generating polynomial formulas and software generating images) are available on
GitHub for interested readers. Thus, this paper is not a comprehensive compact
study that reports the results obtained from comparative experiments but rather
attempts to show a new direction in the field of visualisation with possible
applications in malware analysis.
