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We classify .NET files as either benign or malicious by examining directedgraphs derived from the set of functions comprising the given file. Each graphis viewed probabilistically as a Markov chain where each node represents a codeblock of the corresponding function, and by computing the PageRank vector(Perron vector with transport), a probability measure can be defined over thenodes of the given graph. Each graph is vectorized by computing Lebesgueantiderivatives of hand-engineered functions defined on the vertex set of thegiven graph against the PageRank measure. Files are subsequently vectorized byaggregating the set of vectors corresponding to the set of graphs resultingfrom decompiling the given file. The result is a fast, intuitive, andeasy-to-compute glass-box vectorization scheme, which can be leveraged fortraining a standalone classifier or to augment an existing feature space. Werefer to this vectorization technique as PageRank Measure IntegrationVectorization (PMIV). We demonstrate the efficacy of PMIV by training a vanillarandom forest on 2.5 million samples of decompiled .NET, evenly split betweenbenign and malicious, from our in-house corpus and compare this model to abaseline model which leverages a text-only feature space. The median timeneeded for decompilation and scoring was 24ms.

Applications of Graph Integration to Function Comparison and Malware Classification