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Article overview
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Novel feature extraction, selection and fusion for effective malware family classification | | Mansour Ahmadi
; Giorgio Giacinto
; Dmitry Ulyanov
; Stanislav Semenov
; Mikhail Trofimov
; | | Date: |
13 Nov 2015 | | Abstract: | Modern malware is designed with mutation characteristics, namely polymorphism
and metamorphism, which causes an enormous growth in the number of variants of
malware samples. Categorization of malware samples on the basis of their
behaviors is essential for the computer security community in order to group
samples belonging to same family. Microsoft released a malware classification
challenge in 2015 with a huge dataset of near 0.5 terabytes of data, containing
more than 20K malware samples. The analysis of this dataset inspired the
development of a novel paradigm that is effective in categorizing malware
variants into their actual family groups. This paradigm is presented and
discussed in the present paper, where emphasis has been given to the phases
related to the extraction, and selection of a set of novel features for the
effective representation of malware samples. Features can be grouped according
to different characteristics of malware behavior, and their fusion is performed
according to a per-class weighting paradigm. The proposed method achieved a
very high accuracy ($approx$ 0.998) on the Microsoft Malware Challenge
dataset. | | Source: | arXiv, 1511.4317 | | Services: | Forum | Review | PDF | Favorites | |
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