  Malicious software are categorized into families based on their static and
dynamic characteristics, infection methods, and nature of threat. Visual
exploration of malware instances and families in a low dimensional space helps
in giving a first overview about dependencies and relationships among these
instances, detecting their groups and isolating outliers. Furthermore, visual
exploration of different sets of features is useful in assessing the quality of
these sets to carry a valid abstract representation, which can be later used in
classification and clustering algorithms to achieve a high accuracy. In this
paper, we investigate one of the best dimensionality reduction techniques known
as t-SNE to reduce the malware representation from a high dimensional space
consisting of thousands of features to a low dimensional space. We experiment
with different feature sets and depict malware clusters in 2-D. Surprisingly,
t-SNE does not only provide nice 2-D drawings, but also dramatically increases
the generalization power of SVM classifiers. Moreover, obtained results showed
that cross-validation accuracy is much better using the 2-D embedded
representation of samples than using the original high-dimensional
representation.
