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To date, a large number of research papers have been written on theclassification of malware, its identification, classification into differentfamilies and the distinction between malware and goodware. These works havebeen based on captured malware samples and have attempted to analyse malwareand goodware using various techniques, including techniques from the field ofartificial intelligence. For example, neural networks have played a significantrole in these classification methods. Some of this work also deals withanalysing malware using its visualisation. These works usually convert malwaresamples capturing the structure of malware into image structures, which arethen the object of image processing. In this paper, we propose a veryunconventional and novel approach to malware visualisation based on dynamicbehaviour analysis, with the idea that the images, which are visually veryinteresting, are then used to classify malware concerning goodware. Ourapproach opens an extensive topic for future discussion and provides many newdirections for research in malware analysis and classification, as discussed inconclusion. The results of the presented experiments are based on a database of6 589 997 goodware, 827 853 potentially unwanted applications and 4 174 203malware samples provided by ESET and selected experimental data (images,generating polynomial formulas and software generating images) are available onGitHub for interested readers. Thus, this paper is not a comprehensive compactstudy that reports the results obtained from comparative experiments but ratherattempts to show a new direction in the field of visualisation with possibleapplications in malware analysis.

From Malware Samples to Fractal Images: A New Paradigm for Classification. (Version 2.0, Previous version paper name: Have you ever seen malware?)