Based on the latest statistics, we can see a significant increase in the amount of malware on the market compared to previous years. Companies that produce and maintain antivirus systems receive thousands of samples every day, which need to be analysed. To meet this difficult task, many different tools have been created to allow automatic analysis. In this article, we will focus on one of the techniques for analysing malware samples - namely static analysis. However, this will not be a typical analysis. We will demonstrate that for the analysis and classification of potentially harmful executable files, you can successfully adopt methods known from the analysis, classification and recognition of images. In the "Experiment" section, based on the data set (malicious samples) available in the "Microsoft Malware Classification Challenge", we will conduct a test in which after the learning process based on 5,900 malware samples, we will classify 900 samples to one of 9 families.
Ziubina, Ruslana; Litawa, Grzegorz; Szklarczyk, Rafal; Biel, Patryk; Veselska, Olga; and Nikodem, Joanna, "Detection of Viruses Using Machine Learning Method" (2020). PACIS 2020 Proceedings. 9.
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