Abstract
Verdicts assignment is a recurring problem in malware research and it involves deciding if a given program is clean or infected (if it contains malicious logic). Since the general problem of identifying malicious logic is undecidable, a certain amount of manual analysis is required. As the collections of both clean and malicious samples are continuously increasing, we would like to reduce the manual work to a minimum, by using information extracted by automated analysis systems and the similarity between some programs in the collection.
Based on the assumption that similar programs are likely to share the same verdict, we have designed a system that selects a subset from a given collection of program samples for manual analysis. The selected subset should be as small as possible, given the constraint that the other verdicts must be inferable from the manually-assigned ones. The system was tested on a collection of more than 200000 clusters built using the single linkage approach on a collection of over 20 million samples.
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Oprişa, C., Cabău, G., Sebestyen Pal, G. (2018). Multi-centroid Cluster Analysis in Malware Research. In: Tantar, AA., Tantar, E., Emmerich, M., Legrand, P., Alboaie, L., Luchian, H. (eds) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation VI. Advances in Intelligent Systems and Computing, vol 674. Springer, Cham. https://doi.org/10.1007/978-3-319-69710-9_7
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DOI: https://doi.org/10.1007/978-3-319-69710-9_7
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