  We classify .NET files as either benign or malicious by examining directed
graphs derived from the set of functions comprising the given file. Each graph
is viewed probabilistically as a Markov chain where each node represents a code
block of the corresponding function, and by computing the PageRank vector
(Perron vector with transport), a probability measure can be defined over the
nodes of the given graph. Each graph is vectorized by computing Lebesgue
antiderivatives of hand-engineered functions defined on the vertex set of the
given graph against the PageRank measure. Files are subsequently vectorized by
aggregating the set of vectors corresponding to the set of graphs resulting
from decompiling the given file. The result is a fast, intuitive, and
easy-to-compute glass-box vectorization scheme, which can be leveraged for
training a standalone classifier or to augment an existing feature space. We
refer to this vectorization technique as PageRank Measure Integration
Vectorization (PMIV). We demonstrate the efficacy of PMIV by training a vanilla
random forest on 2.5 million samples of decompiled .NET, evenly split between
benign and malicious, from our in-house corpus and compare this model to a
baseline model which leverages a text-only feature space. The median time
needed for decompilation and scoring was 24ms.
