  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, because they
receive huge number of malware everyday, and the signature extraction process
is usually based on malicious parts characterizing malware families. 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.
